In [5]:
#################################################################################################################################
################################################ MILESTONE PROJECT - PART 1 ######################################################
#1. Create Pandas dataframes
#2. Manipulate data (indexing, selection, variable dtypes)conca
#3. Plot data (Matplotlib, Bokeh)
In [2]:
#Load all libraries that will be used in this project
%matplotlib inline
import matplotlib
import seaborn as sns
matplotlib.rcParams['savefig.dpi'] = 144
In [3]:
import pandas as pd
import json
from io import StringIO
import io
import csv
import sys
import datetime
In [4]:
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import re
In [5]:
from bokeh.plotting import figure, show
from bokeh.io import output_notebook
from bokeh.palettes import Spectral6
from bokeh.resources import CDN
from bokeh.embed import file_html
from ipywidgets import *
In [6]:
from matplotlib import cm
import holoviews as hv
hv.extension('bokeh', 'matplotlib')
In [7]:
#create a dataframe
df1 = pd.DataFrame({
'id':[1,2,3,4,5,6,7,8,9,10,11],
'number': ['16.30', '17.20', '17.70','16.30','17.90','15.90','16.00','18.12','16.40','17.00','16.32'],
'response':[0,1,1,0,0,1,0,1,0,0,0]},
index=[1,2,3,4,5,6,7,8,9,10,11])
In [8]:
#Indexing dataframes by 'id'
df1.set_index(['id']).head(11)
Out[8]:
| number | response | |
|---|---|---|
| id | ||
| 1 | 16.30 | 0 |
| 2 | 17.20 | 1 |
| 3 | 17.70 | 1 |
| 4 | 16.30 | 0 |
| 5 | 17.90 | 0 |
| 6 | 15.90 | 1 |
| 7 | 16.00 | 0 |
| 8 | 18.12 | 1 |
| 9 | 16.40 | 0 |
| 10 | 17.00 | 0 |
| 11 | 16.32 | 0 |
In [9]:
#Let's create a new variable 'date' from current year and range from 2005 to 2015. Then I'll add this new column to each dataframe
year = datetime.datetime.today().year
date = range(2005,year-2)
date
#Also, can create a new variable date which years ranging between 2005 and 2015 as:
#date = pd.date_range('2005', '2016', freq='A')
Out[9]:
[2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015]
In [10]:
#Convert list to an array with float format
date = np.array(date, dtype=np.float32)
date
Out[10]:
array([ 2005., 2006., 2007., 2008., 2009., 2010., 2011., 2012.,
2013., 2014., 2015.], dtype=float32)
In [11]:
#Sort by reverse index
#df1.sort_index(ascending=False).head(11)
In [12]:
#Define object length using the the variable 'number' as reference
sLength = len(df1['number'])
#Add the new variable 'date' to each dataframe
df1['date'] = date
In [13]:
#Sort by'number' values
df1.sort_values('number').head(11)
Out[13]:
| id | number | response | date | |
|---|---|---|---|---|
| 6 | 6 | 15.90 | 1 | 2010.0 |
| 7 | 7 | 16.00 | 0 | 2011.0 |
| 1 | 1 | 16.30 | 0 | 2005.0 |
| 4 | 4 | 16.30 | 0 | 2008.0 |
| 11 | 11 | 16.32 | 0 | 2015.0 |
| 9 | 9 | 16.40 | 0 | 2013.0 |
| 10 | 10 | 17.00 | 0 | 2014.0 |
| 2 | 2 | 17.20 | 1 | 2006.0 |
| 3 | 3 | 17.70 | 1 | 2007.0 |
| 5 | 5 | 17.90 | 0 | 2009.0 |
| 8 | 8 | 18.12 | 1 | 2012.0 |
In [14]:
#convert the variable 'number' to float64
df1[['number']] = df1[['number']].astype('float')
#convert the variable 'year' to int
df1[['date']] = df1[['date']].astype('int')
#output['date'] = pd.to_datetime(output['date'])
#output['date'] = output['date'].apply(lambda x: x.strftime('%Y'))
#Convert "response" variable to boolean
df1[['response']] = df1[['response']].astype('bool') #Also: #output['response'].astype(str).astype(int)
#pd.factorize(output['response'])[0]
In [15]:
#see dtype objects
df1.dtypes
Out[15]:
id int64 number float64 response bool date int64 dtype: object
In [16]:
df1
Out[16]:
| id | number | response | date | |
|---|---|---|---|---|
| 1 | 1 | 16.30 | False | 2005 |
| 2 | 2 | 17.20 | True | 2006 |
| 3 | 3 | 17.70 | True | 2007 |
| 4 | 4 | 16.30 | False | 2008 |
| 5 | 5 | 17.90 | False | 2009 |
| 6 | 6 | 15.90 | True | 2010 |
| 7 | 7 | 16.00 | False | 2011 |
| 8 | 8 | 18.12 | True | 2012 |
| 9 | 9 | 16.40 | False | 2013 |
| 10 | 10 | 17.00 | False | 2014 |
| 11 | 11 | 16.32 | False | 2015 |
In [17]:
df1.set_index(['date']).head(11)
Out[17]:
| id | number | response | |
|---|---|---|---|
| date | |||
| 2005 | 1 | 16.30 | False |
| 2006 | 2 | 17.20 | True |
| 2007 | 3 | 17.70 | True |
| 2008 | 4 | 16.30 | False |
| 2009 | 5 | 17.90 | False |
| 2010 | 6 | 15.90 | True |
| 2011 | 7 | 16.00 | False |
| 2012 | 8 | 18.12 | True |
| 2013 | 9 | 16.40 | False |
| 2014 | 10 | 17.00 | False |
| 2015 | 11 | 16.32 | False |
In [18]:
from ipywidgets import interact
import numpy as np
from bokeh.io import push_notebook
from bokeh.plotting import figure, show, output_notebook
In [20]:
#Plot number vs date using matplotlib
from matplotlib import pylab as plt
df1.sort_values(by='date').plot(x='date', y='number', label=id)
plt.ylabel("number")
plt.legend(loc='lower right')
Out[20]:
<matplotlib.legend.Legend at 0x7f8a3d908410>
In [21]:
#Use Bokeh to create an interactive plot of the temporal trend
output_notebook()
TOOLS = "pan, box_zoom, wheel_zoom, reset, save"
p = figure(tools=TOOLS,title="Temporal trend", plot_height=300, plot_width=600, y_range=(15,20),x_axis_label="date")
r = p.line(x, y, color="#2222aa", line_width=3)
show(p)
In [22]:
#Create two additional dataframes
df2 = pd.DataFrame({
'id':[12,13,14,15,16,17,18,19,20,21,22],
'number': ['18.00', '18.20', '16.90','17.10','16.60','17.50','17.60','17.62','17.30','16.50','17.12'],
'response':[1,1,0,0,1,0,0,1,1,0,1]},
index=[11,12,13,14,15,16,17,18,19,20,21])
df3 = pd.DataFrame({
'id':[23,24,25,26,27,28,29,30,31,32,33],
'number': ['16.00', '15.88', '17.30','15.90','16.00','17.10','16.60','15.92','16.30','16.10','17.42'],
'response':[1,0,0,1,1,0,1,0,1,1,0]},
index=[22,23,24,25,26,27,28,29,30,31,32])
In [23]:
#Indexing dataframes by 'id'
df2.set_index(['id']).head(11)
Out[23]:
| number | response | |
|---|---|---|
| id | ||
| 12 | 18.00 | 1 |
| 13 | 18.20 | 1 |
| 14 | 16.90 | 0 |
| 15 | 17.10 | 0 |
| 16 | 16.60 | 1 |
| 17 | 17.50 | 0 |
| 18 | 17.60 | 0 |
| 19 | 17.62 | 1 |
| 20 | 17.30 | 1 |
| 21 | 16.50 | 0 |
| 22 | 17.12 | 1 |
In [24]:
df3.set_index(['id']).head(11)
Out[24]:
| number | response | |
|---|---|---|
| id | ||
| 23 | 16.00 | 1 |
| 24 | 15.88 | 0 |
| 25 | 17.30 | 0 |
| 26 | 15.90 | 1 |
| 27 | 16.00 | 1 |
| 28 | 17.10 | 0 |
| 29 | 16.60 | 1 |
| 30 | 15.92 | 0 |
| 31 | 16.30 | 1 |
| 32 | 16.10 | 1 |
| 33 | 17.42 | 0 |
In [25]:
#Define object length using the the variable 'number' as reference
sLength = len(df1['number'])
#Add the new variable 'date' to each dataframe
df2['date'] = date
df3['date'] = date
In [26]:
#Concatenate the three dataframes
dfs = [df1, df2, df3]
output = pd.concat(dfs)
#Also, can use merge to combine the three dataframes
#output_join= output.merge(output, on='date', how='left')
#output_join.head(33)
In [27]:
#convert the variable 'number' to float64
output[['number']] = output[['number']].astype('float')
#convert the variable 'year' to int
output[['date']] = output[['date']].astype('int')
#Convert "response" variable to boolean True/False
output[['response']] = output[['response']].astype('bool')
In [28]:
#Indexing by date
output.set_index(['date']).head(33)
Out[28]:
| id | number | response | |
|---|---|---|---|
| date | |||
| 2005 | 1 | 16.30 | False |
| 2006 | 2 | 17.20 | True |
| 2007 | 3 | 17.70 | True |
| 2008 | 4 | 16.30 | False |
| 2009 | 5 | 17.90 | False |
| 2010 | 6 | 15.90 | True |
| 2011 | 7 | 16.00 | False |
| 2012 | 8 | 18.12 | True |
| 2013 | 9 | 16.40 | False |
| 2014 | 10 | 17.00 | False |
| 2015 | 11 | 16.32 | False |
| 2005 | 12 | 18.00 | True |
| 2006 | 13 | 18.20 | True |
| 2007 | 14 | 16.90 | False |
| 2008 | 15 | 17.10 | False |
| 2009 | 16 | 16.60 | True |
| 2010 | 17 | 17.50 | False |
| 2011 | 18 | 17.60 | False |
| 2012 | 19 | 17.62 | True |
| 2013 | 20 | 17.30 | True |
| 2014 | 21 | 16.50 | False |
| 2015 | 22 | 17.12 | True |
| 2005 | 23 | 16.00 | True |
| 2006 | 24 | 15.88 | False |
| 2007 | 25 | 17.30 | False |
| 2008 | 26 | 15.90 | True |
| 2009 | 27 | 16.00 | True |
| 2010 | 28 | 17.10 | False |
| 2011 | 29 | 16.60 | True |
| 2012 | 30 | 15.92 | False |
| 2013 | 31 | 16.30 | True |
| 2014 | 32 | 16.10 | True |
| 2015 | 33 | 17.42 | False |
In [29]:
#A short description of the variable "number" in the combined dataframes
print "Numbers between 2005 and 2015"
output['number'].describe()
Numbers between 2005 and 2015
Out[29]:
count 33.000000 mean 16.851515 std 0.733468 min 15.880000 25% 16.300000 50% 16.900000 75% 17.420000 max 18.200000 Name: number, dtype: float64
In [30]:
#select column 'number' and sum all its values
sum_numb= output['number'].sum()
sum_numb
Out[30]:
556.09999999999991
In [31]:
#How many unique values are there in the dataframe?
output['number'].unique()[:11]
Out[31]:
array([ 16.3 , 17.2 , 17.7 , 17.9 , 15.9 , 16. , 18.12, 16.4 ,
17. , 16.32, 18. ])
In [32]:
#How many cases of each value are there?
output['number'].value_counts().head(33)
Out[32]:
16.00 3 16.30 3 17.30 2 17.10 2 15.90 2 16.60 2 17.42 1 15.92 1 17.00 1 18.00 1 18.12 1 17.50 1 18.20 1 16.50 1 17.60 1 17.62 1 17.70 1 17.90 1 15.88 1 16.90 1 16.10 1 17.12 1 16.32 1 17.20 1 16.40 1 Name: number, dtype: int64
In [35]:
output.plot(x="date", y=["number", "response"], figsize=(12, 8), kind="bar")
Out[35]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f8a3a050310>
In [36]:
#Assign axis names to each dataframe
output = pd.concat(dfs, keys=['x', 'y', 'z'])
output
Out[36]:
| id | number | response | date | ||
|---|---|---|---|---|---|
| x | 1 | 1 | 16.3 | 0 | 2005.0 |
| 2 | 2 | 17.2 | 1 | 2006.0 | |
| 3 | 3 | 17.7 | 1 | 2007.0 | |
| 4 | 4 | 16.3 | 0 | 2008.0 | |
| 5 | 5 | 17.9 | 0 | 2009.0 | |
| 6 | 6 | 15.9 | 1 | 2010.0 | |
| 7 | 7 | 16 | 0 | 2011.0 | |
| 8 | 8 | 18.12 | 1 | 2012.0 | |
| 9 | 9 | 16.4 | 0 | 2013.0 | |
| 10 | 10 | 17 | 0 | 2014.0 | |
| 11 | 11 | 16.32 | 0 | 2015.0 | |
| y | 11 | 12 | 18.00 | 1 | 2005.0 |
| 12 | 13 | 18.20 | 1 | 2006.0 | |
| 13 | 14 | 16.90 | 0 | 2007.0 | |
| 14 | 15 | 17.10 | 0 | 2008.0 | |
| 15 | 16 | 16.60 | 1 | 2009.0 | |
| 16 | 17 | 17.50 | 0 | 2010.0 | |
| 17 | 18 | 17.60 | 0 | 2011.0 | |
| 18 | 19 | 17.62 | 1 | 2012.0 | |
| 19 | 20 | 17.30 | 1 | 2013.0 | |
| 20 | 21 | 16.50 | 0 | 2014.0 | |
| 21 | 22 | 17.12 | 1 | 2015.0 | |
| z | 22 | 23 | 16.00 | 1 | 2005.0 |
| 23 | 24 | 15.88 | 0 | 2006.0 | |
| 24 | 25 | 17.30 | 0 | 2007.0 | |
| 25 | 26 | 15.90 | 1 | 2008.0 | |
| 26 | 27 | 16.00 | 1 | 2009.0 | |
| 27 | 28 | 17.10 | 0 | 2010.0 | |
| 28 | 29 | 16.60 | 1 | 2011.0 | |
| 29 | 30 | 15.92 | 0 | 2012.0 | |
| 30 | 31 | 16.30 | 1 | 2013.0 | |
| 31 | 32 | 16.10 | 1 | 2014.0 | |
| 32 | 33 | 17.42 | 0 | 2015.0 |
In [37]:
#################################################################################################################################
################################################ MILESTONE PROJECT - PART 2 ######################################################
#1. Request data from public API using request and simplejson libraries
#2. Convert jSon data to Pandas dataframe
#3. Create table and plotting data (Bokeh, Holoviews)
In [38]:
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
matplotlib.rcParams['savefig.dpi'] = 144
In [39]:
import pandas as pd
import json
from io import StringIO
import io
import csv
import sys
import datetime
import numpy as np
import re
In [40]:
import simplejson as json
import urllib2
import requests
import ujson as json
In [41]:
import holoviews as hv
hv.extension('bokeh', 'matplotlib')
from bokeh.plotting import figure, show
from bokeh.io import output_notebook
from bokeh.palettes import Spectral6
from ipywidgets import *
In [42]:
#Request search of jobless rate in USA between 1980 and 2016
#API Keys parameters can be found at: http://blog.inqubu.com/inqstats-open-api-published-to-get-demographic-data
r = requests.get('http://inqstatsapi.inqubu.com?api_key=c0081761e5b8e007&data=jobless_rate&countries=us&years=1980:2016')
r
Out[42]:
<Response [200]>
In [43]:
print(r.content)
[
{
"countryCode": "us",
"countryName": "USA",
"jobless_rate": [
{
"year": "2014",
"data": "6.20"
},
{
"year": "2013",
"data": "7.40"
},
{
"year": "2012",
"data": "8.20"
},
{
"year": "2011",
"data": "9.00"
},
{
"year": "2010",
"data": "9.70"
},
{
"year": "2009",
"data": "9.40"
},
{
"year": "2008",
"data": "5.90"
},
{
"year": "2007",
"data": "4.70"
},
{
"year": "2006",
"data": "4.70"
},
{
"year": "2005",
"data": "5.20"
},
{
"year": "2004",
"data": "5.60"
},
{
"year": "2003",
"data": "6.10"
},
{
"year": "2002",
"data": "5.90"
},
{
"year": "2001",
"data": "4.80"
},
{
"year": "2000",
"data": "4.10"
},
{
"year": "1999",
"data": "4.30"
},
{
"year": "1998",
"data": "4.60"
},
{
"year": "1997",
"data": "5.00"
},
{
"year": "1996",
"data": "5.50"
},
{
"year": "1995",
"data": "5.70"
},
{
"year": "1994",
"data": "6.20"
},
{
"year": "1993",
"data": "7.00"
},
{
"year": "1992",
"data": "7.60"
},
{
"year": "1991",
"data": "6.90"
}
]
}
]
In [44]:
data = r.json()
print(type(data))
print(data)
<type 'list'>
[{u'countryName': u'USA', u'countryCode': u'us', u'jobless_rate': [{u'data': u'6.20', u'year': u'2014'}, {u'data': u'7.40', u'year': u'2013'}, {u'data': u'8.20', u'year': u'2012'}, {u'data': u'9.00', u'year': u'2011'}, {u'data': u'9.70', u'year': u'2010'}, {u'data': u'9.40', u'year': u'2009'}, {u'data': u'5.90', u'year': u'2008'}, {u'data': u'4.70', u'year': u'2007'}, {u'data': u'4.70', u'year': u'2006'}, {u'data': u'5.20', u'year': u'2005'}, {u'data': u'5.60', u'year': u'2004'}, {u'data': u'6.10', u'year': u'2003'}, {u'data': u'5.90', u'year': u'2002'}, {u'data': u'4.80', u'year': u'2001'}, {u'data': u'4.10', u'year': u'2000'}, {u'data': u'4.30', u'year': u'1999'}, {u'data': u'4.60', u'year': u'1998'}, {u'data': u'5.00', u'year': u'1997'}, {u'data': u'5.50', u'year': u'1996'}, {u'data': u'5.70', u'year': u'1995'}, {u'data': u'6.20', u'year': u'1994'}, {u'data': u'7.00', u'year': u'1993'}, {u'data': u'7.60', u'year': u'1992'}, {u'data': u'6.90', u'year': u'1991'}]}]
In [45]:
print(r.headers)
print(r.headers["content-type"])
{'Content-Length': '258', 'Content-Encoding': 'gzip', 'Vary': 'Accept-Encoding', 'Keep-Alive': 'timeout=4, max=500', 'Server': 'Apache/2.4', 'Connection': 'Keep-Alive', 'Date': 'Sat, 03 Feb 2018 23:34:44 GMT', 'Access-Control-Allow-Origin': '*', 'Content-Type': 'application/json; charset=utf-8'}
application/json; charset=utf-8
In [46]:
#Define the target url
url="http://inqstatsapi.inqubu.com?api_key=c0081761e5b8e007&data=jobless_rate&countries=us&years=1980:2016"
In [47]:
data = urllib2.urlopen(url).read()
data
Out[47]:
'[\n {\n "countryCode": "us",\n "countryName": "USA",\n "jobless_rate": [\n {\n "year": "2014",\n "data": "6.20"\n },\n {\n "year": "2013",\n "data": "7.40"\n },\n {\n "year": "2012",\n "data": "8.20"\n },\n {\n "year": "2011",\n "data": "9.00"\n },\n {\n "year": "2010",\n "data": "9.70"\n },\n {\n "year": "2009",\n "data": "9.40"\n },\n {\n "year": "2008",\n "data": "5.90"\n },\n {\n "year": "2007",\n "data": "4.70"\n },\n {\n "year": "2006",\n "data": "4.70"\n },\n {\n "year": "2005",\n "data": "5.20"\n },\n {\n "year": "2004",\n "data": "5.60"\n },\n {\n "year": "2003",\n "data": "6.10"\n },\n {\n "year": "2002",\n "data": "5.90"\n },\n {\n "year": "2001",\n "data": "4.80"\n },\n {\n "year": "2000",\n "data": "4.10"\n },\n {\n "year": "1999",\n "data": "4.30"\n },\n {\n "year": "1998",\n "data": "4.60"\n },\n {\n "year": "1997",\n "data": "5.00"\n },\n {\n "year": "1996",\n "data": "5.50"\n },\n {\n "year": "1995",\n "data": "5.70"\n },\n {\n "year": "1994",\n "data": "6.20"\n },\n {\n "year": "1993",\n "data": "7.00"\n },\n {\n "year": "1992",\n "data": "7.60"\n },\n {\n "year": "1991",\n "data": "6.90"\n }\n ]\n }\n]'
In [48]:
#Convert from json format to Python dict and limit variable field
job=json.loads(data)
job=json.loads(data)[0]['jobless_rate']
job
Out[48]:
[{u'data': u'6.20', u'year': u'2014'},
{u'data': u'7.40', u'year': u'2013'},
{u'data': u'8.20', u'year': u'2012'},
{u'data': u'9.00', u'year': u'2011'},
{u'data': u'9.70', u'year': u'2010'},
{u'data': u'9.40', u'year': u'2009'},
{u'data': u'5.90', u'year': u'2008'},
{u'data': u'4.70', u'year': u'2007'},
{u'data': u'4.70', u'year': u'2006'},
{u'data': u'5.20', u'year': u'2005'},
{u'data': u'5.60', u'year': u'2004'},
{u'data': u'6.10', u'year': u'2003'},
{u'data': u'5.90', u'year': u'2002'},
{u'data': u'4.80', u'year': u'2001'},
{u'data': u'4.10', u'year': u'2000'},
{u'data': u'4.30', u'year': u'1999'},
{u'data': u'4.60', u'year': u'1998'},
{u'data': u'5.00', u'year': u'1997'},
{u'data': u'5.50', u'year': u'1996'},
{u'data': u'5.70', u'year': u'1995'},
{u'data': u'6.20', u'year': u'1994'},
{u'data': u'7.00', u'year': u'1993'},
{u'data': u'7.60', u'year': u'1992'},
{u'data': u'6.90', u'year': u'1991'}]
In [49]:
#Export data into a dataframe and print basic numbers
data = pd.DataFrame(job)
data
data.describe()
Out[49]:
| data | year | |
|---|---|---|
| count | 24 | 24 |
| unique | 21 | 24 |
| top | 6.20 | 2012 |
| freq | 2 | 1 |
In [50]:
#Create a new dataframe, index and change name of the first column
dfjob=data[['data', 'year']].head(23)
dfjob.columns
dfjob.columns.values[0] = 'jobless rate'
dfjob
Out[50]:
| jobless rate | year | |
|---|---|---|
| 0 | 6.20 | 2014 |
| 1 | 7.40 | 2013 |
| 2 | 8.20 | 2012 |
| 3 | 9.00 | 2011 |
| 4 | 9.70 | 2010 |
| 5 | 9.40 | 2009 |
| 6 | 5.90 | 2008 |
| 7 | 4.70 | 2007 |
| 8 | 4.70 | 2006 |
| 9 | 5.20 | 2005 |
| 10 | 5.60 | 2004 |
| 11 | 6.10 | 2003 |
| 12 | 5.90 | 2002 |
| 13 | 4.80 | 2001 |
| 14 | 4.10 | 2000 |
| 15 | 4.30 | 1999 |
| 16 | 4.60 | 1998 |
| 17 | 5.00 | 1997 |
| 18 | 5.50 | 1996 |
| 19 | 5.70 | 1995 |
| 20 | 6.20 | 1994 |
| 21 | 7.00 | 1993 |
| 22 | 7.60 | 1992 |
In [51]:
#Convert variable type to assign a sequence argument and plot 'year' vs 'jobless rate'
dfjob=dfjob.astype(float)
In [60]:
dfjob.plot(x='year', y='jobless rate', figsize=(12, 7), kind='barh')
Out[60]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f8a38f6db90>
In [54]:
#Export data and create hv table
table = hv.Table(dfjob, 'year', 'jobless rate')
table
Out[54]:
In [55]:
#Create interactive plot using holoviews
hv.Curve(table) + hv.Bars(table)
Out[55]:
In [129]:
#################################################################################################################################
################################################ MILESTONE PROJECT - PART 3 ######################################################
#1. Request API of stock ticker prices from Quandle WIKI dataset (two approaches with and without selection filters applied)
#2. Convert jSon data to Pandas dataframe
#3. Create two separate dataframes, one for closing prices per ticker and another one for closing, opening, adjusted closing and adjusted openening prices per ticker
#4. Analyze prices data since last month
#5. Create table and plot data (Bokeh, Holoviews)
In [130]:
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
matplotlib.rcParams['savefig.dpi'] = 144
import pandas as pd
import numpy as np
In [131]:
import simplejson as json
import urllib2
import requests
import ujson as json
In [132]:
from bokeh.plotting import figure, show, output_notebook
from bokeh.resources import CDN
from bokeh.embed import file_html
from ipywidgets import interact
from bokeh.io import push_notebook
from matplotlib import cm
import holoviews as hv
hv.extension('bokeh', 'matplotlib')
In [133]:
#################################################################################################################################
#3.1. GENERAL API REQUEST
In [134]:
#Set url
url="https://www.quandl.com/api/v3/datatables/WIKI/PRICES.json?api_key=MhSyqwHb1N6rn5JiB7QF"
In [135]:
#Search request of ticker prices
r = requests.get('https://www.quandl.com/api/v3/datatables/WIKI/PRICES.json?api_key=MhSyqwHb1N6rn5JiB7QF')
r
Out[135]:
<Response [200]>
In [136]:
#Return json file as python dict
ticker = r.json()
ticker
Out[136]:
{u'datatable': {u'columns': [{u'name': u'ticker', u'type': u'String'},
{u'name': u'date', u'type': u'Date'},
{u'name': u'open', u'type': u'BigDecimal(34,12)'},
{u'name': u'high', u'type': u'BigDecimal(34,12)'},
{u'name': u'low', u'type': u'BigDecimal(34,12)'},
{u'name': u'close', u'type': u'BigDecimal(34,12)'},
{u'name': u'volume', u'type': u'BigDecimal(37,15)'},
{u'name': u'ex-dividend', u'type': u'BigDecimal(42,20)'},
{u'name': u'split_ratio', u'type': u'double'},
{u'name': u'adj_open', u'type': u'BigDecimal(50,28)'},
{u'name': u'adj_high', u'type': u'BigDecimal(50,28)'},
{u'name': u'adj_low', u'type': u'BigDecimal(50,28)'},
{u'name': u'adj_close', u'type': u'BigDecimal(50,28)'},
{u'name': u'adj_volume', u'type': u'double'}],
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15.364060250639,
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22.1,
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15.077519162483,
15.13209841737,
14.900136584101,
15.002472687014,
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[u'A',
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23.47,
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23.0,
3772400.0,
0.0,
1.0,
15.759759848568,
16.01218890242,
15.159388044813,
15.69153577996,
3772400.0],
[u'A',
u'2003-10-06',
23.0,
23.23,
22.81,
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977600.0,
0.0,
1.0,
15.69153577996,
15.84845113776,
15.561910049604,
15.712003000542,
977600.0],
[u'A',
u'2003-10-07',
22.9,
23.56,
22.7,
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1940500.0,
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1.0,
15.623311711351,
16.073590564168,
15.486863574134,
15.984899274977,
1940500.0],
[u'A',
u'2003-10-08',
23.68,
23.75,
23.22,
23.41,
1645900.0,
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1.0,
16.155459446498,
16.203216294524,
15.841628730899,
15.971254461255,
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[u'A',
u'2003-10-09',
23.41,
23.96,
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0.0,
1.0,
15.971254461255,
16.346486838602,
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16.121347412194,
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[u'A',
u'2003-10-10',
23.54,
23.79,
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894800.0,
0.0,
1.0,
16.059945750446,
16.230505921967,
16.019011309281,
16.16910426022,
894800.0],
[u'A',
u'2003-10-13',
23.8,
23.98,
23.7,
23.88,
963500.0,
0.0,
1.0,
16.237328328828,
16.360131652323,
16.16910426022,
16.291907583715,
963500.0],
[u'A',
u'2003-10-14',
23.8,
24.15,
23.72,
24.07,
1592400.0,
0.0,
1.0,
16.237328328828,
16.476112568958,
16.182749073941,
16.421533314071,
1592400.0],
[u'A',
u'2003-10-15',
24.37,
24.5,
24.04,
24.24,
2216000.0,
0.0,
1.0,
16.626205519897,
16.714896809088,
16.401066093489,
16.537514230706,
2216000.0],
[u'A',
u'2003-10-16',
24.2,
24.39,
24.02,
24.32,
1662800.0,
0.0,
1.0,
16.510224603262,
16.639850333618,
16.387421279767,
16.592093485592,
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[u'A',
u'2003-10-17',
24.29,
24.29,
23.84,
23.98,
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0.0,
1.0,
16.57162626501,
16.57162626501,
16.264617956272,
16.360131652323,
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[u'A',
u'2003-10-20',
23.9,
23.99,
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23.83,
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0.0,
1.0,
16.305552397437,
16.366954059184,
16.134992225915,
16.257795549411,
926400.0],
[u'A',
u'2003-10-21',
23.77,
24.22,
23.7,
24.02,
1531900.0,
0.0,
1.0,
16.216861108246,
16.523869416984,
16.16910426022,
16.387421279767,
1531900.0],
[u'A',
u'2003-10-22',
23.8,
23.84,
23.3,
23.48,
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0.0,
1.0,
16.237328328828,
16.264617956272,
15.896207985786,
16.019011309281,
1055100.0],
[u'A',
u'2003-10-23',
23.48,
23.48,
22.65,
22.97,
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0.0,
1.0,
16.019011309281,
16.019011309281,
15.45275153983,
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[u'A',
u'2003-10-24',
22.97,
23.09,
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1.0,
15.671068559377,
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15.45275153983,
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[u'A',
u'2003-10-27',
23.12,
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1.0,
15.77340466229,
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[u'A',
u'2003-10-28',
23.0,
24.0,
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0.0,
1.0,
15.69153577996,
16.373776466045,
15.677890966238,
16.373776466045,
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[u'A',
u'2003-10-29',
23.99,
24.19,
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0.0,
1.0,
16.366954059184,
16.503402196401,
16.230505921967,
16.435178127793,
1791900.0],
[u'A',
u'2003-10-30',
24.25,
25.31,
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u'2003-10-31',
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u'2003-11-03',
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[u'A',
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[u'A',
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[u'A',
u'2003-11-06',
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[u'A',
u'2003-11-07',
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[u'A',
u'2003-11-10',
26.7,
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[u'A',
u'2003-11-11',
25.7,
26.2,
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17.53358563239,
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1864700.0],
...]},
u'meta': {u'next_cursor_id': u'djFfMTAwMDFfMTUxNzc3MjAwMw=='}}
In [137]:
ticker_data=ticker['datatable']
ticker_data.keys()
Out[137]:
[u'data', u'columns']
In [138]:
#See keys
ticker.keys()
Out[138]:
[u'meta', u'datatable']
In [139]:
#FROM HERE ON
#################################################################################################################################
#FOLLOW A) OR B)
In [140]:
#################################################################################################################################
In [141]:
#3.1.A) OBTAIN A DATAFRAME OF CLOSING PRICES OF EACH TICKER FROM THE LAST MONTH
#Select last month prices
ticker_col=ticker_data['columns']
ticker1=ticker_data['data'][4557:4578]
ticker2=ticker_data['data'][4872:4893]
ticker3=ticker_data['data'][7981:8002]
ticker4=ticker_data['data'][9274:9292]
import pandas
df1=pandas.DataFrame.from_dict(ticker1, orient='columns')
df1.drop(df1.columns[[2,3,4,6,7,8,9,10,11,12,13]], axis=1, inplace=True)
df1.columns = ['Ticker', 'Date', 'Price']
df2=pandas.DataFrame.from_dict(ticker2, orient='columns')
df2.drop(df2.columns[[2,3,4,6,7,8,9,10,11,12,13]], axis=1, inplace=True)
df2.columns = ['Ticker', 'Date', 'Price']
df3=pandas.DataFrame.from_dict(ticker3, orient='columns')
df3.drop(df3.columns[[2,3,4,6,7,8,9,10,11,12,13]], axis=1, inplace=True)
df3.columns = ['Ticker', 'Date', 'Price']
df4=pandas.DataFrame.from_dict(ticker4, orient='columns')
df4.drop(df4.columns[[2,3,4,6,7,8,9,10,11,12,13]], axis=1, inplace=True)
df4.columns = ['Ticker', 'Date', 'Price']
dfs = [df1, df2, df3, df4]
dfs = pd.concat(dfs)
In [142]:
out=dfs.pivot(index= 'Date', columns='Ticker', values='Price')
out
Out[142]:
| Ticker | A | AA | AAL | AAMC |
|---|---|---|---|---|
| Date | ||||
| 2018-01-02 | 67.60 | 55.17 | 52.990 | NaN |
| 2018-01-03 | 69.32 | 54.50 | 52.330 | 77.9500 |
| 2018-01-04 | 68.80 | 54.70 | 52.670 | 80.2000 |
| 2018-01-05 | 69.90 | 54.09 | 52.650 | 80.2000 |
| 2018-01-08 | 70.05 | 55.00 | 52.130 | 80.2000 |
| 2018-01-09 | 71.77 | 54.20 | 52.080 | 79.0000 |
| 2018-01-10 | 70.79 | 56.17 | 53.780 | 79.0000 |
| 2018-01-11 | 70.80 | 56.91 | 56.420 | 79.9900 |
| 2018-01-12 | 71.73 | 56.76 | 58.470 | NaN |
| 2018-01-16 | 71.23 | 56.24 | 57.985 | 75.9500 |
| 2018-01-17 | 72.06 | 56.99 | 58.160 | 71.5010 |
| 2018-01-18 | 72.19 | 53.00 | 58.340 | 71.5010 |
| 2018-01-19 | 73.07 | 53.10 | 58.060 | NaN |
| 2018-01-22 | 73.48 | 52.94 | 58.100 | 66.1803 |
| 2018-01-23 | 73.44 | 52.49 | 58.290 | 67.7000 |
| 2018-01-24 | 73.58 | 53.30 | 54.790 | 69.6647 |
| 2018-01-25 | 73.86 | 53.11 | 53.050 | 69.6647 |
| 2018-01-26 | 74.82 | 54.00 | 53.070 | 68.1500 |
| 2018-01-29 | 74.53 | 54.49 | 52.680 | 68.1500 |
| 2018-01-30 | 72.99 | 52.52 | 52.590 | 69.0241 |
| 2018-01-31 | 73.43 | 52.02 | 54.320 | 69.0241 |
In [16]:
out.plot.box(figsize=(8, 5))
Out[16]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fb65cb301d0>
In [17]:
#Bar plots of Date per Ticker prices
out.plot.bar(colormap='Greens',stacked=True, figsize=(11, 7),legend='top_right')
Out[17]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fb65c6eaf10>
In [ ]:
#Plot closing prices for each ticker
In [18]:
names = [('Price', 'Stock Input')]
ds = hv.Dataset(dfs, ['Date', 'Ticker'], names)
In [19]:
%%opts Curve [width=800 height=550] {+framewise}
ds.to(hv.Curve, 'Date','Price')
Out[19]:
In [20]:
%%opts Bars [width=900 height=400 tools=['hover'] group_index=1 legend_position='top_right']
Tickers = ['A', 'AA', 'AAL', 'AAMC']
ds.select(Ticker=Tickers).to(hv.Bars, ['Date','Ticker'], 'Price').sort()
Out[20]:
In [21]:
%%opts Curve [width=200] (color='indianred')
Tickers = ['A', 'AA', 'AAL', 'AAMC']
grouped = ds.select(Ticker=Tickers).to(hv.Curve, 'Date', 'Price')
grouped.grid('Ticker')
Out[21]:
In [22]:
%%opts Curve [width=600] (color=Cycle(values=['indianred', 'slateblue', 'lightseagreen', 'coral']))
grouped.overlay('Ticker')
Out[22]:
In [ ]:
#JUMP TO LINE 34 FOR ADDITIONAL SUMMARY STATISTICS AND GRAPH VISUALIZATION
In [24]:
#################################################################################################################################
In [114]:
#3.1.B) OBTAIN A DATAFRAME OF CLOSING, OPENING, ADJUSTED CLOSING AND ADJUSTED OPENING PRICRES FROM THE LAST MONTH FOR EACH TICKER
#Select last month prices
ticker_col=ticker_data['columns']
ticker1=ticker_data['data'][4557:4578]
ticker2=ticker_data['data'][4872:4893]
ticker3=ticker_data['data'][7981:8002]
ticker4=ticker_data['data'][9274:9292]
import pandas
df1=pandas.DataFrame.from_dict(ticker1, orient='columns')
df1.drop(df1.columns[[3,4,6,7,8,10,11,13]], axis=1, inplace=True)
df1.columns = ['Ticker', 'Date', 'Open Price', 'Close Price','Adj Open Price', 'Adj Close Price']
df2=pandas.DataFrame.from_dict(ticker2, orient='columns')
df2.drop(df2.columns[[3,4,6,7,8,10,11,13]], axis=1, inplace=True)
df2.columns = ['Ticker', 'Date', 'Open Price', 'Close Price','Adj Open Price', 'Adj Close Price']
df3=pandas.DataFrame.from_dict(ticker3, orient='columns')
df3.drop(df3.columns[[3,4,6,7,8,10,11,13]], axis=1, inplace=True)
df3.columns = ['Ticker', 'Date', 'Open Price', 'Close Price','Adj Open Price', 'Adj Close Price']
df4=pandas.DataFrame.from_dict(ticker4, orient='columns')
df4.drop(df4.columns[[3,4,6,7,8,10,11,13]], axis=1, inplace=True)
df4.columns = ['Ticker', 'Date', 'Open Price', 'Close Price','Adj Open Price', 'Adj Close Price']
dfs = [df1, df2, df3, df4]
dfs = pd.concat(dfs)
dfs
Out[114]:
| Ticker | Date | Open Price | Close Price | Adj Open Price | Adj Close Price | |
|---|---|---|---|---|---|---|
| 0 | A | 2018-01-02 | 67.4200 | 67.6000 | 67.4200 | 67.6000 |
| 1 | A | 2018-01-03 | 67.6200 | 69.3200 | 67.6200 | 69.3200 |
| 2 | A | 2018-01-04 | 69.5400 | 68.8000 | 69.5400 | 68.8000 |
| 3 | A | 2018-01-05 | 68.7300 | 69.9000 | 68.7300 | 69.9000 |
| 4 | A | 2018-01-08 | 69.7300 | 70.0500 | 69.7300 | 70.0500 |
| 5 | A | 2018-01-09 | 70.6800 | 71.7700 | 70.6800 | 71.7700 |
| 6 | A | 2018-01-10 | 71.4500 | 70.7900 | 71.4500 | 70.7900 |
| 7 | A | 2018-01-11 | 70.9200 | 70.8000 | 70.9200 | 70.8000 |
| 8 | A | 2018-01-12 | 70.7300 | 71.7300 | 70.7300 | 71.7300 |
| 9 | A | 2018-01-16 | 72.0200 | 71.2300 | 72.0200 | 71.2300 |
| 10 | A | 2018-01-17 | 71.7200 | 72.0600 | 71.7200 | 72.0600 |
| 11 | A | 2018-01-18 | 72.2000 | 72.1900 | 72.2000 | 72.1900 |
| 12 | A | 2018-01-19 | 72.4800 | 73.0700 | 72.4800 | 73.0700 |
| 13 | A | 2018-01-22 | 73.1700 | 73.4800 | 73.1700 | 73.4800 |
| 14 | A | 2018-01-23 | 74.0500 | 73.4400 | 74.0500 | 73.4400 |
| 15 | A | 2018-01-24 | 73.6600 | 73.5800 | 73.6600 | 73.5800 |
| 16 | A | 2018-01-25 | 74.1700 | 73.8600 | 74.1700 | 73.8600 |
| 17 | A | 2018-01-26 | 74.3000 | 74.8200 | 74.3000 | 74.8200 |
| 18 | A | 2018-01-29 | 74.4800 | 74.5300 | 74.4800 | 74.5300 |
| 19 | A | 2018-01-30 | 73.9900 | 72.9900 | 73.9900 | 72.9900 |
| 20 | A | 2018-01-31 | 73.7700 | 73.4300 | 73.7700 | 73.4300 |
| 0 | AA | 2018-01-02 | 54.0600 | 55.1700 | 54.0600 | 55.1700 |
| 1 | AA | 2018-01-03 | 54.9200 | 54.5000 | 54.9200 | 54.5000 |
| 2 | AA | 2018-01-04 | 54.8100 | 54.7000 | 54.8100 | 54.7000 |
| 3 | AA | 2018-01-05 | 54.6500 | 54.0900 | 54.6500 | 54.0900 |
| 4 | AA | 2018-01-08 | 53.9600 | 55.0000 | 53.9600 | 55.0000 |
| 5 | AA | 2018-01-09 | 55.0000 | 54.2000 | 55.0000 | 54.2000 |
| 6 | AA | 2018-01-10 | 54.3700 | 56.1700 | 54.3700 | 56.1700 |
| 7 | AA | 2018-01-11 | 56.6000 | 56.9100 | 56.6000 | 56.9100 |
| 8 | AA | 2018-01-12 | 57.0500 | 56.7600 | 57.0500 | 56.7600 |
| ... | ... | ... | ... | ... | ... | ... |
| 9 | AAL | 2018-01-16 | 58.7900 | 57.9850 | 58.7900 | 57.9850 |
| 10 | AAL | 2018-01-17 | 58.3100 | 58.1600 | 58.3100 | 58.1600 |
| 11 | AAL | 2018-01-18 | 58.0000 | 58.3400 | 58.0000 | 58.3400 |
| 12 | AAL | 2018-01-19 | 58.5900 | 58.0600 | 58.5900 | 58.0600 |
| 13 | AAL | 2018-01-22 | 57.9900 | 58.1000 | 57.9900 | 58.1000 |
| 14 | AAL | 2018-01-23 | 57.7400 | 58.2900 | 57.7400 | 58.2900 |
| 15 | AAL | 2018-01-24 | 54.3500 | 54.7900 | 54.3500 | 54.7900 |
| 16 | AAL | 2018-01-25 | 54.0000 | 53.0500 | 54.0000 | 53.0500 |
| 17 | AAL | 2018-01-26 | 53.6500 | 53.0700 | 53.6500 | 53.0700 |
| 18 | AAL | 2018-01-29 | 52.7900 | 52.6800 | 52.7900 | 52.6800 |
| 19 | AAL | 2018-01-30 | 52.4500 | 52.5900 | 52.4500 | 52.5900 |
| 20 | AAL | 2018-01-31 | 53.0800 | 54.3200 | 53.0800 | 54.3200 |
| 0 | AAMC | 2018-01-03 | 77.9500 | 77.9500 | 77.9500 | 77.9500 |
| 1 | AAMC | 2018-01-04 | 80.2000 | 80.2000 | 80.2000 | 80.2000 |
| 2 | AAMC | 2018-01-05 | 80.2000 | 80.2000 | 80.2000 | 80.2000 |
| 3 | AAMC | 2018-01-08 | 80.2000 | 80.2000 | 80.2000 | 80.2000 |
| 4 | AAMC | 2018-01-09 | 79.0000 | 79.0000 | 79.0000 | 79.0000 |
| 5 | AAMC | 2018-01-10 | 79.0000 | 79.0000 | 79.0000 | 79.0000 |
| 6 | AAMC | 2018-01-11 | 79.9900 | 79.9900 | 79.9900 | 79.9900 |
| 7 | AAMC | 2018-01-16 | 75.9500 | 75.9500 | 75.9500 | 75.9500 |
| 8 | AAMC | 2018-01-17 | 71.5010 | 71.5010 | 71.5010 | 71.5010 |
| 9 | AAMC | 2018-01-18 | 71.5010 | 71.5010 | 71.5010 | 71.5010 |
| 10 | AAMC | 2018-01-22 | 69.4000 | 66.1803 | 69.4000 | 66.1803 |
| 11 | AAMC | 2018-01-23 | 67.7000 | 67.7000 | 67.7000 | 67.7000 |
| 12 | AAMC | 2018-01-24 | 69.6500 | 69.6647 | 69.6500 | 69.6647 |
| 13 | AAMC | 2018-01-25 | 69.6647 | 69.6647 | 69.6647 | 69.6647 |
| 14 | AAMC | 2018-01-26 | 67.9000 | 68.1500 | 67.9000 | 68.1500 |
| 15 | AAMC | 2018-01-29 | 68.1500 | 68.1500 | 68.1500 | 68.1500 |
| 16 | AAMC | 2018-01-30 | 66.9500 | 69.0241 | 66.9500 | 69.0241 |
| 17 | AAMC | 2018-01-31 | 69.0241 | 69.0241 | 69.0241 | 69.0241 |
81 rows × 6 columns
In [115]:
#Create a new dataframe 'Date' from previous dataframe that will be used as an index
dfsd=dfs.loc[:, 'Date']
dfsd1=dfsd.drop_duplicates()
dfsd1
Out[115]:
0 2018-01-02 1 2018-01-03 2 2018-01-04 3 2018-01-05 4 2018-01-08 5 2018-01-09 6 2018-01-10 7 2018-01-11 8 2018-01-12 9 2018-01-16 10 2018-01-17 11 2018-01-18 12 2018-01-19 13 2018-01-22 14 2018-01-23 15 2018-01-24 16 2018-01-25 17 2018-01-26 18 2018-01-29 19 2018-01-30 20 2018-01-31 Name: Date, dtype: object
In [116]:
#Re-order the columns in the dataframe:
#1. Assign multi-index columns from tuples
multicols = pd.MultiIndex.from_tuples([('A','Open Price'),('A','Close Price'),('A','Adj Open Price'),('A','Adj Close Price'),
('AA','Open Price'),('AA','Close Price'),('AA','Adj Open Price'),('AA','Adj Close Price'),
('AAL','Open Price'),('AAL','Close Price'),('AAL','Adj Open Price'),('AAL','Adj Close Price'),
('AAMC','Open Price'),('AAMC','Close Price'),('AAMC','Adj Open Price'),('AAMC','Adj Close Price')],
names=['Ticker', 'Price'])
#2. Create an empty dataframe using the former newly created 'Date' index and the multi-index columns from step 1
index = dfsd1
out = pd.DataFrame(index=index,columns=multicols).sort_index().sort_index(axis=1)
out
Out[116]:
| Ticker | A | AA | AAL | AAMC | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Price | Adj Close Price | Adj Open Price | Close Price | Open Price | Adj Close Price | Adj Open Price | Close Price | Open Price | Adj Close Price | Adj Open Price | Close Price | Open Price | Adj Close Price | Adj Open Price | Close Price | Open Price |
| Date | ||||||||||||||||
| 2018-01-02 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2018-01-03 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2018-01-04 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2018-01-05 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2018-01-08 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2018-01-09 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2018-01-10 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2018-01-11 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2018-01-12 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2018-01-16 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2018-01-17 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2018-01-18 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2018-01-19 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2018-01-22 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2018-01-23 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2018-01-24 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2018-01-25 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2018-01-26 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2018-01-29 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2018-01-30 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2018-01-31 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
In [111]:
dfs['Open Price'] = dfs['Open Price'].fillna(dfs['Open Price'].mean())
dfs['Close Price'] = dfs['Close Price'].fillna(dfs['Close Price'].mean())
dfs['Adj Open Price'] = dfs['Adj Open Price'].fillna(dfs['Adj Open Price'].mean())
dfs['Adj Close Price'] = dfs['Adj Close Price'].fillna(dfs['Adj Close Price'].mean())
dfs=dfs[['Open Price', 'Close Price', 'Adj Open Price', 'Adj Close Price']]
In [121]:
#3. Create another dataframe based on the combined dataframes (1,2,3,4, dfs) containing the actual values of ticker prices
#Group columns by 'Ticker' name, pivot column index rows to column and imputate missing cases
dfill = (dfs.groupby('Ticker').apply(lambda g:g.set_index('Date') [['Open Price', 'Close Price', 'Adj Open Price', 'Adj Close Price']]).unstack(level=0).fillna(dfs['Close Price'].mean()))
#Delete column and adjust the multi-index column
dfill.columns=dfill.columns.droplevel()
print dfill
Ticker A AA AAL AAMC A AA AAL AAMC \ Date 2018-01-02 67.42 54.06 52.33 63.298085 67.60 55.17 52.990 63.298085 2018-01-03 67.62 54.92 52.86 77.950000 69.32 54.50 52.330 77.950000 2018-01-04 69.54 54.81 52.48 80.200000 68.80 54.70 52.670 80.200000 2018-01-05 68.73 54.65 52.78 80.200000 69.90 54.09 52.650 80.200000 2018-01-08 69.73 53.96 52.60 80.200000 70.05 55.00 52.130 80.200000 2018-01-09 70.68 55.00 52.97 79.000000 71.77 54.20 52.080 79.000000 2018-01-10 71.45 54.37 53.23 79.000000 70.79 56.17 53.780 79.000000 2018-01-11 70.92 56.60 54.49 79.990000 70.80 56.91 56.420 79.990000 2018-01-12 70.73 57.05 56.56 63.298085 71.73 56.76 58.470 63.298085 2018-01-16 72.02 56.09 58.79 75.950000 71.23 56.24 57.985 75.950000 2018-01-17 71.72 56.03 58.31 71.501000 72.06 56.99 58.160 71.501000 2018-01-18 72.20 53.67 58.00 71.501000 72.19 53.00 58.340 71.501000 2018-01-19 72.48 52.86 58.59 63.298085 73.07 53.10 58.060 63.298085 2018-01-22 73.17 52.80 57.99 69.400000 73.48 52.94 58.100 66.180300 2018-01-23 74.05 52.34 57.74 67.700000 73.44 52.49 58.290 67.700000 2018-01-24 73.66 52.90 54.35 69.650000 73.58 53.30 54.790 69.664700 2018-01-25 74.17 53.74 54.00 69.664700 73.86 53.11 53.050 69.664700 2018-01-26 74.30 53.55 53.65 67.900000 74.82 54.00 53.070 68.150000 2018-01-29 74.48 53.89 52.79 68.150000 74.53 54.49 52.680 68.150000 2018-01-30 73.99 53.67 52.45 66.950000 72.99 52.52 52.590 69.024100 2018-01-31 73.77 52.75 53.08 69.024100 73.43 52.02 54.320 69.024100 Ticker A AA AAL AAMC A AA AAL AAMC Date 2018-01-02 67.42 54.06 52.33 63.298085 67.60 55.17 52.990 63.298085 2018-01-03 67.62 54.92 52.86 77.950000 69.32 54.50 52.330 77.950000 2018-01-04 69.54 54.81 52.48 80.200000 68.80 54.70 52.670 80.200000 2018-01-05 68.73 54.65 52.78 80.200000 69.90 54.09 52.650 80.200000 2018-01-08 69.73 53.96 52.60 80.200000 70.05 55.00 52.130 80.200000 2018-01-09 70.68 55.00 52.97 79.000000 71.77 54.20 52.080 79.000000 2018-01-10 71.45 54.37 53.23 79.000000 70.79 56.17 53.780 79.000000 2018-01-11 70.92 56.60 54.49 79.990000 70.80 56.91 56.420 79.990000 2018-01-12 70.73 57.05 56.56 63.298085 71.73 56.76 58.470 63.298085 2018-01-16 72.02 56.09 58.79 75.950000 71.23 56.24 57.985 75.950000 2018-01-17 71.72 56.03 58.31 71.501000 72.06 56.99 58.160 71.501000 2018-01-18 72.20 53.67 58.00 71.501000 72.19 53.00 58.340 71.501000 2018-01-19 72.48 52.86 58.59 63.298085 73.07 53.10 58.060 63.298085 2018-01-22 73.17 52.80 57.99 69.400000 73.48 52.94 58.100 66.180300 2018-01-23 74.05 52.34 57.74 67.700000 73.44 52.49 58.290 67.700000 2018-01-24 73.66 52.90 54.35 69.650000 73.58 53.30 54.790 69.664700 2018-01-25 74.17 53.74 54.00 69.664700 73.86 53.11 53.050 69.664700 2018-01-26 74.30 53.55 53.65 67.900000 74.82 54.00 53.070 68.150000 2018-01-29 74.48 53.89 52.79 68.150000 74.53 54.49 52.680 68.150000 2018-01-30 73.99 53.67 52.45 66.950000 72.99 52.52 52.590 69.024100 2018-01-31 73.77 52.75 53.08 69.024100 73.43 52.02 54.320 69.024100
In [122]:
#Reset the indices of the two dataframes
out = out.reset_index(drop=True)
dfill = dfill.reset_index(drop=True)
#Rebuild the empty dataframe
out = pd.DataFrame(out)
#Assign length of columns and copy price values within each price category and ticker from one to another dataframe
sLength = len(out['A'])
out['A']= dfill['A'].values
out['AA']= dfill['AA'].values
out['AAL']= dfill['AAL'].values
out['AAMC']= dfill['AAMC'].values
In [123]:
#Add the column 'Date' indexed in the dataframe
out['Date'] = dfsd1
out
Out[123]:
| Ticker | A | AA | AAL | AAMC | Date | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Price | Adj Close Price | Adj Open Price | Close Price | Open Price | Adj Close Price | Adj Open Price | Close Price | Open Price | Adj Close Price | Adj Open Price | Close Price | Open Price | Adj Close Price | Adj Open Price | Close Price | Open Price | |
| 0 | 67.42 | 67.60 | 67.42 | 67.60 | 54.06 | 55.17 | 54.06 | 55.17 | 52.33 | 52.990 | 52.33 | 52.990 | 63.298085 | 63.298085 | 63.298085 | 63.298085 | 2018-01-02 |
| 1 | 67.62 | 69.32 | 67.62 | 69.32 | 54.92 | 54.50 | 54.92 | 54.50 | 52.86 | 52.330 | 52.86 | 52.330 | 77.950000 | 77.950000 | 77.950000 | 77.950000 | 2018-01-03 |
| 2 | 69.54 | 68.80 | 69.54 | 68.80 | 54.81 | 54.70 | 54.81 | 54.70 | 52.48 | 52.670 | 52.48 | 52.670 | 80.200000 | 80.200000 | 80.200000 | 80.200000 | 2018-01-04 |
| 3 | 68.73 | 69.90 | 68.73 | 69.90 | 54.65 | 54.09 | 54.65 | 54.09 | 52.78 | 52.650 | 52.78 | 52.650 | 80.200000 | 80.200000 | 80.200000 | 80.200000 | 2018-01-05 |
| 4 | 69.73 | 70.05 | 69.73 | 70.05 | 53.96 | 55.00 | 53.96 | 55.00 | 52.60 | 52.130 | 52.60 | 52.130 | 80.200000 | 80.200000 | 80.200000 | 80.200000 | 2018-01-08 |
| 5 | 70.68 | 71.77 | 70.68 | 71.77 | 55.00 | 54.20 | 55.00 | 54.20 | 52.97 | 52.080 | 52.97 | 52.080 | 79.000000 | 79.000000 | 79.000000 | 79.000000 | 2018-01-09 |
| 6 | 71.45 | 70.79 | 71.45 | 70.79 | 54.37 | 56.17 | 54.37 | 56.17 | 53.23 | 53.780 | 53.23 | 53.780 | 79.000000 | 79.000000 | 79.000000 | 79.000000 | 2018-01-10 |
| 7 | 70.92 | 70.80 | 70.92 | 70.80 | 56.60 | 56.91 | 56.60 | 56.91 | 54.49 | 56.420 | 54.49 | 56.420 | 79.990000 | 79.990000 | 79.990000 | 79.990000 | 2018-01-11 |
| 8 | 70.73 | 71.73 | 70.73 | 71.73 | 57.05 | 56.76 | 57.05 | 56.76 | 56.56 | 58.470 | 56.56 | 58.470 | 63.298085 | 63.298085 | 63.298085 | 63.298085 | 2018-01-12 |
| 9 | 72.02 | 71.23 | 72.02 | 71.23 | 56.09 | 56.24 | 56.09 | 56.24 | 58.79 | 57.985 | 58.79 | 57.985 | 75.950000 | 75.950000 | 75.950000 | 75.950000 | 2018-01-16 |
| 10 | 71.72 | 72.06 | 71.72 | 72.06 | 56.03 | 56.99 | 56.03 | 56.99 | 58.31 | 58.160 | 58.31 | 58.160 | 71.501000 | 71.501000 | 71.501000 | 71.501000 | 2018-01-17 |
| 11 | 72.20 | 72.19 | 72.20 | 72.19 | 53.67 | 53.00 | 53.67 | 53.00 | 58.00 | 58.340 | 58.00 | 58.340 | 71.501000 | 71.501000 | 71.501000 | 71.501000 | 2018-01-18 |
| 12 | 72.48 | 73.07 | 72.48 | 73.07 | 52.86 | 53.10 | 52.86 | 53.10 | 58.59 | 58.060 | 58.59 | 58.060 | 63.298085 | 63.298085 | 63.298085 | 63.298085 | 2018-01-19 |
| 13 | 73.17 | 73.48 | 73.17 | 73.48 | 52.80 | 52.94 | 52.80 | 52.94 | 57.99 | 58.100 | 57.99 | 58.100 | 69.400000 | 66.180300 | 69.400000 | 66.180300 | 2018-01-22 |
| 14 | 74.05 | 73.44 | 74.05 | 73.44 | 52.34 | 52.49 | 52.34 | 52.49 | 57.74 | 58.290 | 57.74 | 58.290 | 67.700000 | 67.700000 | 67.700000 | 67.700000 | 2018-01-23 |
| 15 | 73.66 | 73.58 | 73.66 | 73.58 | 52.90 | 53.30 | 52.90 | 53.30 | 54.35 | 54.790 | 54.35 | 54.790 | 69.650000 | 69.664700 | 69.650000 | 69.664700 | 2018-01-24 |
| 16 | 74.17 | 73.86 | 74.17 | 73.86 | 53.74 | 53.11 | 53.74 | 53.11 | 54.00 | 53.050 | 54.00 | 53.050 | 69.664700 | 69.664700 | 69.664700 | 69.664700 | 2018-01-25 |
| 17 | 74.30 | 74.82 | 74.30 | 74.82 | 53.55 | 54.00 | 53.55 | 54.00 | 53.65 | 53.070 | 53.65 | 53.070 | 67.900000 | 68.150000 | 67.900000 | 68.150000 | 2018-01-26 |
| 18 | 74.48 | 74.53 | 74.48 | 74.53 | 53.89 | 54.49 | 53.89 | 54.49 | 52.79 | 52.680 | 52.79 | 52.680 | 68.150000 | 68.150000 | 68.150000 | 68.150000 | 2018-01-29 |
| 19 | 73.99 | 72.99 | 73.99 | 72.99 | 53.67 | 52.52 | 53.67 | 52.52 | 52.45 | 52.590 | 52.45 | 52.590 | 66.950000 | 69.024100 | 66.950000 | 69.024100 | 2018-01-30 |
| 20 | 73.77 | 73.43 | 73.77 | 73.43 | 52.75 | 52.02 | 52.75 | 52.02 | 53.08 | 54.320 | 53.08 | 54.320 | 69.024100 | 69.024100 | 69.024100 | 69.024100 | 2018-01-31 |
In [124]:
out.describe()
Out[124]:
| Ticker | A | AA | AAL | AAMC | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Price | Adj Close Price | Adj Open Price | Close Price | Open Price | Adj Close Price | Adj Open Price | Close Price | Open Price | Adj Close Price | Adj Open Price | Close Price | Open Price | Adj Close Price | Adj Open Price | Close Price | Open Price |
| count | 21.000000 | 21.000000 | 21.000000 | 21.000000 | 21.000000 | 21.000000 | 21.000000 | 21.000000 | 21.000000 | 21.000000 | 21.000000 | 21.000000 | 21.000000 | 21.000000 | 21.000000 | 21.000000 |
| mean | 71.753810 | 71.878095 | 71.753810 | 71.878095 | 54.271905 | 54.366667 | 54.271905 | 54.366667 | 54.763810 | 54.902619 | 54.763810 | 54.902619 | 72.086907 | 72.044960 | 72.086907 | 72.044960 |
| std | 2.208258 | 1.963269 | 2.208258 | 1.963269 | 1.319730 | 1.548106 | 1.319730 | 1.548106 | 2.444354 | 2.581703 | 2.444354 | 2.581703 | 6.100375 | 6.135201 | 6.100375 | 6.135201 |
| min | 67.420000 | 67.600000 | 67.420000 | 67.600000 | 52.340000 | 52.020000 | 52.340000 | 52.020000 | 52.330000 | 52.080000 | 52.330000 | 52.080000 | 63.298085 | 63.298085 | 63.298085 | 63.298085 |
| 25% | 70.680000 | 70.790000 | 70.680000 | 70.790000 | 53.550000 | 53.100000 | 53.550000 | 53.100000 | 52.790000 | 52.670000 | 52.790000 | 52.670000 | 67.900000 | 68.150000 | 67.900000 | 68.150000 |
| 50% | 72.020000 | 72.060000 | 72.020000 | 72.060000 | 53.960000 | 54.200000 | 53.960000 | 54.200000 | 53.650000 | 53.780000 | 53.650000 | 53.780000 | 69.664700 | 69.664700 | 69.664700 | 69.664700 |
| 75% | 73.770000 | 73.440000 | 73.770000 | 73.440000 | 54.920000 | 55.170000 | 54.920000 | 55.170000 | 57.740000 | 58.060000 | 57.740000 | 58.060000 | 79.000000 | 79.000000 | 79.000000 | 79.000000 |
| max | 74.480000 | 74.820000 | 74.480000 | 74.820000 | 57.050000 | 56.990000 | 57.050000 | 56.990000 | 58.790000 | 58.470000 | 58.790000 | 58.470000 | 80.200000 | 80.200000 | 80.200000 | 80.200000 |
In [32]:
out.columns
Out[32]:
MultiIndex(levels=[[u'A', u'AA', u'AAL', u'AAMC', u'Date'], [u'Adj Close Price', u'Adj Open Price', u'Close Price', u'Open Price', u'']],
labels=[[0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4], [0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 4]],
names=[u'Ticker', u'Price'])
In [33]:
#FROM HERE ON THE RESULTS CHANGE DEPENDING ON WHETHER YOU CHOSE 3.1.A, 3.1.B!!!!!
################################################################################################################################
#RESULTS SUMMARY STATISTICS AND DATA PLOT
In [34]:
#Show summary statistics
print out.describe()
#Calculate/plot mean-error values of each Ticker
mean = out.mean()
error = out.std()
mean
error
Ticker A AA \ Price Adj Close Price Adj Open Price Close Price Open Price Adj Close Price count 21.000000 21.000000 21.000000 21.000000 21.000000 mean 71.753810 71.878095 71.753810 71.878095 54.271905 std 2.208258 1.963269 2.208258 1.963269 1.319730 min 67.420000 67.600000 67.420000 67.600000 52.340000 25% 70.680000 70.790000 70.680000 70.790000 53.550000 50% 72.020000 72.060000 72.020000 72.060000 53.960000 75% 73.770000 73.440000 73.770000 73.440000 54.920000 max 74.480000 74.820000 74.480000 74.820000 57.050000 Ticker AAL \ Price Adj Open Price Close Price Open Price Adj Close Price Adj Open Price count 21.000000 21.000000 21.000000 21.000000 21.000000 mean 54.366667 54.271905 54.366667 54.763810 54.902619 std 1.548106 1.319730 1.548106 2.444354 2.581703 min 52.020000 52.340000 52.020000 52.330000 52.080000 25% 53.100000 53.550000 53.100000 52.790000 52.670000 50% 54.200000 53.960000 54.200000 53.650000 53.780000 75% 55.170000 54.920000 55.170000 57.740000 58.060000 max 56.990000 57.050000 56.990000 58.790000 58.470000 Ticker AAMC \ Price Close Price Open Price Adj Close Price Adj Open Price Close Price count 21.000000 21.000000 21.000000 21.000000 21.000000 mean 54.763810 54.902619 72.075207 72.033259 72.075207 std 2.444354 2.581703 6.118120 6.152761 6.118120 min 52.330000 52.080000 63.216183 63.216183 63.216183 25% 52.790000 52.670000 67.900000 68.150000 67.900000 50% 53.650000 53.780000 69.664700 69.664700 69.664700 75% 57.740000 58.060000 79.000000 79.000000 79.000000 max 58.790000 58.470000 80.200000 80.200000 80.200000 Ticker Price Open Price count 21.000000 mean 72.033259 std 6.152761 min 63.216183 25% 68.150000 50% 69.664700 75% 79.000000 max 80.200000
Out[34]:
Ticker Price
A Adj Close Price 2.208258
Adj Open Price 1.963269
Close Price 2.208258
Open Price 1.963269
AA Adj Close Price 1.319730
Adj Open Price 1.548106
Close Price 1.319730
Open Price 1.548106
AAL Adj Close Price 2.444354
Adj Open Price 2.581703
Close Price 2.444354
Open Price 2.581703
AAMC Adj Close Price 6.118120
Adj Open Price 6.152761
Close Price 6.118120
Open Price 6.152761
dtype: float64
In [35]:
#Mean+Error bar plot
fig, ax = plt.subplots()
mean.plot.bar(yerr=error, ax=ax)
Out[35]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fb65c320390>
In [36]:
out.plot.area(stacked=False,figsize=(11, 8))
Out[36]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fb65c038dd0>
In [37]:
out.plot(subplots=True, figsize=(10, 10))
Out[37]:
array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fb654d8a7d0>,
<matplotlib.axes._subplots.AxesSubplot object at 0x7fb654ce2bd0>,
<matplotlib.axes._subplots.AxesSubplot object at 0x7fb654c67dd0>,
<matplotlib.axes._subplots.AxesSubplot object at 0x7fb65c36c790>,
<matplotlib.axes._subplots.AxesSubplot object at 0x7fb65c2c2110>,
<matplotlib.axes._subplots.AxesSubplot object at 0x7fb654c2f7d0>,
<matplotlib.axes._subplots.AxesSubplot object at 0x7fb654bb0a10>,
<matplotlib.axes._subplots.AxesSubplot object at 0x7fb654bd54d0>,
<matplotlib.axes._subplots.AxesSubplot object at 0x7fb654ab0650>,
<matplotlib.axes._subplots.AxesSubplot object at 0x7fb654a475d0>,
<matplotlib.axes._subplots.AxesSubplot object at 0x7fb6549b0110>,
<matplotlib.axes._subplots.AxesSubplot object at 0x7fb6549342d0>,
<matplotlib.axes._subplots.AxesSubplot object at 0x7fb654894a10>,
<matplotlib.axes._subplots.AxesSubplot object at 0x7fb654818bd0>,
<matplotlib.axes._subplots.AxesSubplot object at 0x7fb6547970d0>,
<matplotlib.axes._subplots.AxesSubplot object at 0x7fb65471c290>], dtype=object)
In [38]:
plt.figure()
out.plot(colormap=cm.cubehelix,figsize=(12, 7))
Out[38]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fb65415d7d0>
<matplotlib.figure.Figure at 0x7fb65416b790>
In [40]:
################################################################################################################################
#3.2.SPECIFIC API REQUEST (Filter by columns 'date', 'ticker' and ticker prices)
In [1]:
%matplotlib inline
import matplotlib
import seaborn as sns
matplotlib.rcParams['savefig.dpi'] = 144
In [2]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import simplejson as json
import urllib2
import requests
import ujson as json
In [3]:
from bokeh.plotting import figure
from bokeh.resources import CDN
from bokeh.embed import file_html
from matplotlib import cm
import holoviews as hv
hv.extension('bokeh', 'matplotlib')
In [4]:
#Either use:
#3.2.1.A)
import quandl
quandl.ApiConfig.api_key = 'MhSyqwHb1N6rn5JiB7QF'
dfs=quandl.get_table('WIKI/PRICES',qopts={'columns': ['date','ticker','open','close','adj_open','adj_close']},date='2018-1-1,2018-1-2,2018-1-3,2018-1-4,2018-1-5,2018-1-6,2018-1-7,2018-1-8,2018-1-9,2018-1-10,2018-1-11,2018-1-12,2018-1-13,2018-1-14,2018-1-15,2018-1-16,2018-1-17,2018-1-18,2018-1-19,2018-1-20,2018-1-21,2018-1-22,2018-1-23,2018-1-24,2018-1-25,2018-1-26,2018-1-27,2018-1-28,2018-1-29,2018-1-30,2018-1-31')
#For closing prices only alternatively you can use:
#dfs=quandl.get_table('WIKI/PRICES',qopts={'columns': ['date','ticker','close']},date='2018-1-1,2018-1-2,2018-1-3,2018-1-4,2018-1-5,2018-1-6,2018-1-7,2018-1-8,2018-1-9,2018-1-10,2018-1-11,2018-1-12,2018-1-13,2018-1-14,2018-1-15,2018-1-16,2018-1-17,2018-1-18,2018-1-19,2018-1-20,2018-1-21,2018-1-22,2018-1-23,2018-1-24,2018-1-25,2018-1-26,2018-1-27,2018-1-28,2018-1-29,2018-1-30,2018-1-31')
#or
#3.2.1.B)
#r = requests.get('https://www.quandl.com/api/v3/datatables/WIKI/PRICES.json?qopts.columns=ticker,date,open,close,adj_open,adj_close&date=2018-1-1,2018-1-2,2018-1-3,2018-1-4,2018-1-5,2018-1-6,2018-1-7,2018-1-8,2018-1-9,2018-1-10,2018-1-11,2018-1-12,2018-1-13,2018-1-14,2018-1-15,2018-1-16,2018-1-17,2018-1-18,2018-1-19,2018-1-20,2018-1-21,2018-1-22,2018-1-23,2018-1-24,2018-1-25,2018-1-26,2018-1-27,2018-1-28,2018-1-29,2018-1-30,2018-1-31&api_key=MhSyqwHb1N6rn5JiB7QF')
#qt = r.json()
In [5]:
#From here on follow 3.2.1 or 3.2.2 approaches
#################################################################################################################################
In [6]:
###########3.2.1. This method will result in four plots for each of the price values contained in the requested data
In [7]:
#Rename columns
dfs.columns = ['Date','Ticker','Open Price','Close Price','Adj Open Price','Adj Close Price']
#For search selection filtered only by closing prices use:
#dfs.columns = ['Date','Ticker','Price']
#out=dfs.pivot(index= 'Date', columns='Ticker', values='Price')
In [8]:
#Make four copies of the dataframe and drop columns by price values
dfs_open=dfs.copy(deep=True)
dfs_close=dfs.copy(deep=True)
dfs_adj_open=dfs.copy(deep=True)
dfs_adj_close=dfs.copy(deep=True)
dfs_open=dfs_open.drop(dfs_open.columns[[3, 4, 5]], axis=1)
dfs_close=dfs_close.drop(dfs_close.columns[[2, 4, 5]], axis=1)
dfs_adj_open=dfs_adj_open.drop(dfs_adj_open.columns[[2, 3, 5]], axis=1)
dfs_adj_close=dfs_adj_close.drop(dfs_adj_close.columns[[2, 3, 4]], axis=1)
In [9]:
#Assign Y names
names = [('Open Price', 'Open Price')]
ds_open = hv.Dataset(dfs_open, ['Date', 'Ticker'], names)
names = [('Close Price', 'Close Price')]
ds_close = hv.Dataset(dfs_close, ['Date', 'Ticker'], names)
names = [('Adj Open Price', 'Adj Open Price')]
ds_adj_open = hv.Dataset(dfs_adj_open, ['Date', 'Ticker'], names)
names = [('Adj Close Price', 'Adj Close Price')]
ds_adj_close = hv.Dataset(dfs_adj_close, ['Date', 'Ticker'], names)
In [10]:
#Plot open prices for each ticker
In [11]:
%%opts Curve [width=800 height=550] {+framewise}
%%opts Curve (color='red' line_width=1.5)
ds_open.to(hv.Curve, 'Date','Open Price',label='Last month opening ticker prices')
Out[11]:
In [12]:
#Plot closing prices for each ticker
In [13]:
%%opts Curve [width=800 height=550] {+framewise}
%%opts Curve (color='blue' line_width=1.5)
ds_close.to(hv.Curve, 'Date','Close Price',label='Last month closing ticker prices')
Out[13]:
In [14]:
#Plot adjusted open prices for each ticker
In [15]:
%%opts Curve [width=800 height=550] {+framewise}
%%opts Curve (color='green' line_width=1.5)
ds_adj_open.to(hv.Curve, 'Date','Adj Open Price',label='Last month adjusted opening ticker prices')
Out[15]:
In [16]:
#Plot adjusted closing prices for each ticker
In [17]:
%%opts Curve [width=800 height=550] {+framewise}
%%opts Curve (color='yellow' line_width=1.5)
ds_adj_close.to(hv.Curve, 'Date','Adj Close Price',label='Last month adjusted closing ticker prices')
Out[17]:
In [18]:
import param,paramnb
class Result(param.Parameterized):
score=param.Integer(default=1,bounds=(0,10))
verified=param.Boolean(default=False)
grade=param.ObjectSelector(objects=["A","B","C"])
paramnb.Widgets(Result,next_n=1)
In [ ]:
###########3.2.2. This method aims to categorize a dataframe containing all the prices and tickers for subsequent graph contruction
In [44]:
#Create a new dataframe 'date' from previous dataframe that will be used as an index
dfsd=dfs.loc[:, 'date']
dfsd1=dfsd.drop_duplicates()
dfsd1
Out[44]:
None 0 2018-01-02 1 2018-01-03 2 2018-01-04 3 2018-01-05 4 2018-01-08 5 2018-01-09 6 2018-01-10 7 2018-01-11 8 2018-01-12 9 2018-01-16 10 2018-01-17 11 2018-01-18 12 2018-01-19 13 2018-01-22 14 2018-01-23 15 2018-01-24 16 2018-01-25 17 2018-01-26 18 2018-01-29 19 2018-01-30 20 2018-01-31 Name: date, dtype: datetime64[ns]
In [45]:
#Create a new dataframe using grouping variables 'ticker' and 'prices' and indexed by date. Imputate missing cases with closing prices mean
outg = (dfs.groupby('ticker').apply(lambda g:g.set_index('date')[['open','close','adj_open','adj_close']]).unstack(level=0).fillna(dfs['close'].mean()))
#Delete column and adjust the multi-index column
print outg
open \
ticker A AA AAL AAMC AAN AAOI AAON AAP
date
2018-01-02 67.42 54.06 52.33 724.662246 39.81 37.80 36.95 100.90
2018-01-03 67.62 54.92 52.86 77.950000 39.43 38.27 36.95 106.42
2018-01-04 69.54 54.81 52.48 80.200000 39.98 37.95 37.70 107.75
2018-01-05 68.73 54.65 52.78 80.200000 39.85 38.70 37.55 111.96
2018-01-08 69.73 53.96 52.60 80.200000 39.83 38.90 37.95 112.05
2018-01-09 70.68 55.00 52.97 79.000000 40.82 38.14 37.45 111.69
2018-01-10 71.45 54.37 53.23 79.000000 40.09 35.04 36.90 110.27
2018-01-11 70.92 56.60 54.49 79.990000 39.16 35.37 36.15 110.70
2018-01-12 70.73 57.05 56.56 724.662246 41.19 36.31 36.40 114.38
2018-01-16 72.02 56.09 58.79 75.950000 41.54 35.69 36.75 116.76
2018-01-17 71.72 56.03 58.31 71.501000 41.48 34.00 36.30 114.74
2018-01-18 72.20 53.67 58.00 71.501000 40.42 33.73 36.25 115.67
2018-01-19 72.48 52.86 58.59 724.662246 40.60 34.87 36.10 116.24
2018-01-22 73.17 52.80 57.99 69.400000 42.08 34.98 37.20 116.88
2018-01-23 74.05 52.34 57.74 67.700000 42.30 34.42 36.30 117.74
2018-01-24 73.66 52.90 54.35 69.650000 41.19 34.92 36.20 119.26
2018-01-25 74.17 53.74 54.00 69.664700 41.80 34.32 36.10 121.12
2018-01-26 74.30 53.55 53.65 67.900000 41.86 34.91 36.20 123.34
2018-01-29 74.48 53.89 52.79 68.150000 42.50 34.63 36.10 123.52
2018-01-30 73.99 53.67 52.45 66.950000 41.39 32.74 36.00 121.57
2018-01-31 73.77 52.75 53.08 69.024100 42.13 33.01 36.20 119.86
... adj_close \
ticker AAPL AAT ... CLVS CLW CLX CMA CMC
date ...
2018-01-02 170.160 38.30 ... 66.05 46.20 144.99 86.57 22.77
2018-01-03 172.530 38.19 ... 66.64 46.15 143.43 86.54 24.30
2018-01-04 172.540 37.81 ... 67.72 46.80 145.69 87.51 24.48
2018-01-05 173.440 37.15 ... 66.49 48.40 145.73 88.47 24.90
2018-01-08 174.350 37.11 ... 62.98 49.80 146.44 88.72 25.28
2018-01-09 174.550 37.20 ... 62.01 50.15 146.16 90.37 25.04
2018-01-10 173.160 36.37 ... 62.78 49.35 144.43 91.31 25.27
2018-01-11 174.590 36.36 ... 59.88 50.30 141.99 92.52 25.82
2018-01-12 176.180 36.34 ... 59.01 49.40 140.96 93.04 25.16
2018-01-16 177.900 36.16 ... 55.41 47.85 142.41 93.79 24.59
2018-01-17 176.150 36.18 ... 54.93 49.20 144.32 94.76 24.84
2018-01-18 179.370 36.27 ... 55.87 49.20 143.14 93.81 25.09
2018-01-19 178.610 35.73 ... 56.76 50.25 143.86 94.99 24.82
2018-01-22 177.300 36.22 ... 60.67 49.50 144.48 95.21 25.02
2018-01-23 177.300 36.40 ... 64.10 49.05 143.52 95.25 24.88
2018-01-24 177.250 36.61 ... 61.25 48.30 142.25 95.53 24.99
2018-01-25 174.505 36.24 ... 61.40 47.60 146.13 94.70 24.87
2018-01-26 172.000 36.25 ... 61.75 47.35 142.64 95.81 25.27
2018-01-29 170.160 35.63 ... 62.39 46.90 140.96 95.15 25.26
2018-01-30 165.525 34.90 ... 60.97 47.60 141.76 94.76 24.55
2018-01-31 166.870 34.99 ... 60.50 47.05 141.69 95.22 24.04
ticker CMCO CMCSA CME CMG CMI
date
2018-01-02 41.15 41.07 144.79 292.95 177.060000
2018-01-03 40.81 40.41 147.11 309.00 179.010000
2018-01-04 40.66 40.67 148.68 307.11 181.750000
2018-01-05 40.28 41.04 149.65 313.79 181.460000
2018-01-08 41.38 40.48 151.73 318.47 182.120000
2018-01-09 43.54 40.61 152.63 319.37 181.960000
2018-01-10 42.60 41.09 152.90 321.80 180.560000
2018-01-11 43.00 42.60 152.81 325.50 183.900000
2018-01-12 42.91 42.44 152.73 327.34 184.180000
2018-01-16 42.47 41.82 152.20 327.37 182.550000
2018-01-17 42.82 41.68 151.45 334.63 183.690000
2018-01-18 42.79 41.85 152.21 337.53 183.720000
2018-01-19 44.42 42.50 154.28 343.87 189.080000
2018-01-22 44.25 42.89 155.45 330.00 189.580000
2018-01-23 43.17 42.44 154.25 328.71 188.430000
2018-01-24 42.57 42.99 154.99 329.53 188.350000
2018-01-25 42.44 42.14 154.17 333.97 189.400000
2018-01-26 42.05 42.80 155.16 330.23 192.500000
2018-01-29 41.81 41.98 153.45 332.33 190.810000
2018-01-30 41.41 42.34 153.33 322.08 187.250000
2018-01-31 40.95 42.53 153.48 324.76 724.662246
[21 rows x 1908 columns]
In [46]:
#Remove index
outg = outg.reset_index(drop=True)
#Add the previously generated dataframe index to the final dataframe
outg['date'] = dfsd1
#View the final table
outg
Out[46]:
| open | ... | adj_close | date | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ticker | A | AA | AAL | AAMC | AAN | AAOI | AAON | AAP | AAPL | AAT | ... | CLW | CLX | CMA | CMC | CMCO | CMCSA | CME | CMG | CMI | |
| 0 | 67.42 | 54.06 | 52.33 | 724.662246 | 39.81 | 37.80 | 36.95 | 100.90 | 170.160 | 38.30 | ... | 46.20 | 144.99 | 86.57 | 22.77 | 41.15 | 41.07 | 144.79 | 292.95 | 177.060000 | 2018-01-02 |
| 1 | 67.62 | 54.92 | 52.86 | 77.950000 | 39.43 | 38.27 | 36.95 | 106.42 | 172.530 | 38.19 | ... | 46.15 | 143.43 | 86.54 | 24.30 | 40.81 | 40.41 | 147.11 | 309.00 | 179.010000 | 2018-01-03 |
| 2 | 69.54 | 54.81 | 52.48 | 80.200000 | 39.98 | 37.95 | 37.70 | 107.75 | 172.540 | 37.81 | ... | 46.80 | 145.69 | 87.51 | 24.48 | 40.66 | 40.67 | 148.68 | 307.11 | 181.750000 | 2018-01-04 |
| 3 | 68.73 | 54.65 | 52.78 | 80.200000 | 39.85 | 38.70 | 37.55 | 111.96 | 173.440 | 37.15 | ... | 48.40 | 145.73 | 88.47 | 24.90 | 40.28 | 41.04 | 149.65 | 313.79 | 181.460000 | 2018-01-05 |
| 4 | 69.73 | 53.96 | 52.60 | 80.200000 | 39.83 | 38.90 | 37.95 | 112.05 | 174.350 | 37.11 | ... | 49.80 | 146.44 | 88.72 | 25.28 | 41.38 | 40.48 | 151.73 | 318.47 | 182.120000 | 2018-01-08 |
| 5 | 70.68 | 55.00 | 52.97 | 79.000000 | 40.82 | 38.14 | 37.45 | 111.69 | 174.550 | 37.20 | ... | 50.15 | 146.16 | 90.37 | 25.04 | 43.54 | 40.61 | 152.63 | 319.37 | 181.960000 | 2018-01-09 |
| 6 | 71.45 | 54.37 | 53.23 | 79.000000 | 40.09 | 35.04 | 36.90 | 110.27 | 173.160 | 36.37 | ... | 49.35 | 144.43 | 91.31 | 25.27 | 42.60 | 41.09 | 152.90 | 321.80 | 180.560000 | 2018-01-10 |
| 7 | 70.92 | 56.60 | 54.49 | 79.990000 | 39.16 | 35.37 | 36.15 | 110.70 | 174.590 | 36.36 | ... | 50.30 | 141.99 | 92.52 | 25.82 | 43.00 | 42.60 | 152.81 | 325.50 | 183.900000 | 2018-01-11 |
| 8 | 70.73 | 57.05 | 56.56 | 724.662246 | 41.19 | 36.31 | 36.40 | 114.38 | 176.180 | 36.34 | ... | 49.40 | 140.96 | 93.04 | 25.16 | 42.91 | 42.44 | 152.73 | 327.34 | 184.180000 | 2018-01-12 |
| 9 | 72.02 | 56.09 | 58.79 | 75.950000 | 41.54 | 35.69 | 36.75 | 116.76 | 177.900 | 36.16 | ... | 47.85 | 142.41 | 93.79 | 24.59 | 42.47 | 41.82 | 152.20 | 327.37 | 182.550000 | 2018-01-16 |
| 10 | 71.72 | 56.03 | 58.31 | 71.501000 | 41.48 | 34.00 | 36.30 | 114.74 | 176.150 | 36.18 | ... | 49.20 | 144.32 | 94.76 | 24.84 | 42.82 | 41.68 | 151.45 | 334.63 | 183.690000 | 2018-01-17 |
| 11 | 72.20 | 53.67 | 58.00 | 71.501000 | 40.42 | 33.73 | 36.25 | 115.67 | 179.370 | 36.27 | ... | 49.20 | 143.14 | 93.81 | 25.09 | 42.79 | 41.85 | 152.21 | 337.53 | 183.720000 | 2018-01-18 |
| 12 | 72.48 | 52.86 | 58.59 | 724.662246 | 40.60 | 34.87 | 36.10 | 116.24 | 178.610 | 35.73 | ... | 50.25 | 143.86 | 94.99 | 24.82 | 44.42 | 42.50 | 154.28 | 343.87 | 189.080000 | 2018-01-19 |
| 13 | 73.17 | 52.80 | 57.99 | 69.400000 | 42.08 | 34.98 | 37.20 | 116.88 | 177.300 | 36.22 | ... | 49.50 | 144.48 | 95.21 | 25.02 | 44.25 | 42.89 | 155.45 | 330.00 | 189.580000 | 2018-01-22 |
| 14 | 74.05 | 52.34 | 57.74 | 67.700000 | 42.30 | 34.42 | 36.30 | 117.74 | 177.300 | 36.40 | ... | 49.05 | 143.52 | 95.25 | 24.88 | 43.17 | 42.44 | 154.25 | 328.71 | 188.430000 | 2018-01-23 |
| 15 | 73.66 | 52.90 | 54.35 | 69.650000 | 41.19 | 34.92 | 36.20 | 119.26 | 177.250 | 36.61 | ... | 48.30 | 142.25 | 95.53 | 24.99 | 42.57 | 42.99 | 154.99 | 329.53 | 188.350000 | 2018-01-24 |
| 16 | 74.17 | 53.74 | 54.00 | 69.664700 | 41.80 | 34.32 | 36.10 | 121.12 | 174.505 | 36.24 | ... | 47.60 | 146.13 | 94.70 | 24.87 | 42.44 | 42.14 | 154.17 | 333.97 | 189.400000 | 2018-01-25 |
| 17 | 74.30 | 53.55 | 53.65 | 67.900000 | 41.86 | 34.91 | 36.20 | 123.34 | 172.000 | 36.25 | ... | 47.35 | 142.64 | 95.81 | 25.27 | 42.05 | 42.80 | 155.16 | 330.23 | 192.500000 | 2018-01-26 |
| 18 | 74.48 | 53.89 | 52.79 | 68.150000 | 42.50 | 34.63 | 36.10 | 123.52 | 170.160 | 35.63 | ... | 46.90 | 140.96 | 95.15 | 25.26 | 41.81 | 41.98 | 153.45 | 332.33 | 190.810000 | 2018-01-29 |
| 19 | 73.99 | 53.67 | 52.45 | 66.950000 | 41.39 | 32.74 | 36.00 | 121.57 | 165.525 | 34.90 | ... | 47.60 | 141.76 | 94.76 | 24.55 | 41.41 | 42.34 | 153.33 | 322.08 | 187.250000 | 2018-01-30 |
| 20 | 73.77 | 52.75 | 53.08 | 69.024100 | 42.13 | 33.01 | 36.20 | 119.86 | 166.870 | 34.99 | ... | 47.05 | 141.69 | 95.22 | 24.04 | 40.95 | 42.53 | 153.48 | 324.76 | 724.662246 | 2018-01-31 |
21 rows × 1909 columns
In [47]:
#See elements ordered
sorted(outg)
Out[47]:
[(u'adj_close', u'A'), (u'adj_close', u'AA'), (u'adj_close', u'AAL'), (u'adj_close', u'AAMC'), (u'adj_close', u'AAN'), (u'adj_close', u'AAOI'), (u'adj_close', u'AAON'), (u'adj_close', u'AAP'), (u'adj_close', u'AAPL'), (u'adj_close', u'AAT'), (u'adj_close', u'AAWW'), (u'adj_close', u'ABAX'), (u'adj_close', u'ABBV'), (u'adj_close', u'ABC'), (u'adj_close', u'ABCB'), (u'adj_close', u'ABG'), (u'adj_close', u'ABM'), (u'adj_close', u'ABMD'), (u'adj_close', u'ABT'), (u'adj_close', u'ACAD'), (u'adj_close', u'ACC'), (u'adj_close', u'ACCO'), (u'adj_close', u'ACET'), (u'adj_close', u'ACGL'), (u'adj_close', u'ACHC'), (u'adj_close', u'ACHN'), (u'adj_close', u'ACIW'), (u'adj_close', u'ACLS'), (u'adj_close', u'ACM'), (u'adj_close', u'ACN'), (u'adj_close', u'ACOR'), (u'adj_close', u'ACRE'), (u'adj_close', u'ACRX'), (u'adj_close', u'ACTG'), (u'adj_close', u'ACXM'), (u'adj_close', u'ADBE'), (u'adj_close', u'ADC'), (u'adj_close', u'ADES'), (u'adj_close', u'ADI'), (u'adj_close', u'ADM'), (u'adj_close', u'ADMS'), (u'adj_close', u'ADP'), (u'adj_close', u'ADS'), (u'adj_close', u'ADSK'), (u'adj_close', u'ADTN'), (u'adj_close', u'ADUS'), (u'adj_close', u'AE'), (u'adj_close', u'AEE'), (u'adj_close', u'AEGN'), (u'adj_close', u'AEIS'), (u'adj_close', u'AEL'), (u'adj_close', u'AEO'), (u'adj_close', u'AEP'), (u'adj_close', u'AERI'), (u'adj_close', u'AES'), (u'adj_close', u'AET'), (u'adj_close', u'AFAM'), (u'adj_close', u'AFG'), (u'adj_close', u'AFH'), (u'adj_close', u'AFL'), (u'adj_close', u'AFSI'), (u'adj_close', u'AGCO'), (u'adj_close', u'AGEN'), (u'adj_close', u'AGII'), (u'adj_close', u'AGIO'), (u'adj_close', u'AGM'), (u'adj_close', u'AGN'), (u'adj_close', u'AGNC'), (u'adj_close', u'AGO'), (u'adj_close', u'AGTC'), (u'adj_close', u'AGX'), (u'adj_close', u'AGYS'), (u'adj_close', u'AHC'), (u'adj_close', u'AHH'), (u'adj_close', u'AHL'), (u'adj_close', u'AHP'), (u'adj_close', u'AHT'), (u'adj_close', u'AI'), (u'adj_close', u'AIG'), (u'adj_close', u'AIMC'), (u'adj_close', u'AIN'), (u'adj_close', 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(u'adj_open', u'BECN'), (u'adj_open', u'BELFB'), (u'adj_open', u'BEN'), (u'adj_open', u'BERY'), (u'adj_open', u'BFAM'), (u'adj_open', u'BFIN'), (u'adj_open', u'BFS'), (u'adj_open', u'BF_B'), (u'adj_open', u'BG'), (u'adj_open', u'BGC'), (u'adj_open', u'BGCP'), (u'adj_open', u'BGFV'), (u'adj_open', u'BGG'), (u'adj_open', u'BGS'), (u'adj_open', u'BH'), (u'adj_open', u'BHB'), (u'adj_open', u'BHE'), (u'adj_open', u'BHF'), (u'adj_open', u'BHGE'), (u'adj_open', u'BHLB'), (u'adj_open', u'BID'), (u'adj_open', u'BIDU'), (u'adj_open', u'BIG'), (u'adj_open', u'BIIB'), (u'adj_open', u'BIO'), (u'adj_open', u'BIOL'), (u'adj_open', u'BIOS'), (u'adj_open', u'BJRI'), (u'adj_open', u'BK'), (u'adj_open', u'BKCC'), (u'adj_open', u'BKD'), (u'adj_open', u'BKE'), (u'adj_open', u'BKH'), (u'adj_open', u'BKMU'), (u'adj_open', u'BKS'), (u'adj_open', u'BKU'), (u'adj_open', u'BLDR'), (u'adj_open', u'BLK'), (u'adj_open', u'BLKB'), (u'adj_open', u'BLL'), (u'adj_open', u'BLMN'), (u'adj_open', u'BLUE'), (u'adj_open', u'BLX'), (u'adj_open', u'BMI'), (u'adj_open', u'BMRC'), (u'adj_open', u'BMRN'), (u'adj_open', u'BMS'), (u'adj_open', u'BMTC'), (u'adj_open', u'BMY'), (u'adj_open', u'BNCL'), (u'adj_open', u'BNFT'), (u'adj_open', u'BOBE'), (u'adj_open', u'BOFI'), (u'adj_open', u'BOH'), (u'adj_open', u'BOKF'), (u'adj_open', u'BOOM'), (u'adj_open', u'BP'), (u'adj_open', u'BPFH'), (u'adj_open', u'BPI'), (u'adj_open', u'BPOP'), (u'adj_open', u'BPTH'), (u'adj_open', u'BR'), (u'adj_open', u'BRC'), (u'adj_open', u'BREW'), (u'adj_open', u'BRKL'), (u'adj_open', u'BRKR'), (u'adj_open', u'BRKS'), (u'adj_open', u'BRK_A'), (u'adj_open', u'BRK_B'), (u'adj_open', u'BRO'), (u'adj_open', u'BRS'), (u'adj_open', u'BRSS'), (u'adj_open', u'BRT'), (u'adj_open', u'BSET'), (u'adj_open', u'BSFT'), (u'adj_open', u'BSRR'), (u'adj_open', u'BSTC'), (u'adj_open', u'BSX'), (u'adj_open', u'BTU'), (u'adj_open', u'BTX'), (u'adj_open', u'BURL'), (u'adj_open', u'BUSE'), (u'adj_open', u'BV'), (u'adj_open', u'BWA'), (u'adj_open', u'BWINB'), (u'adj_open', u'BWLD'), (u'adj_open', u'BXC'), (u'adj_open', u'BXP'), (u'adj_open', u'BXS'), (u'adj_open', u'BYD'), (u'adj_open', u'BZH'), (u'adj_open', u'C'), (u'adj_open', u'CA'), (u'adj_open', u'CAC'), (u'adj_open', u'CACC'), (u'adj_open', u'CACI'), (u'adj_open', u'CAG'), (u'adj_open', u'CAH'), (u'adj_open', u'CAKE'), (u'adj_open', u'CALD'), (u'adj_open', u'CALL'), (u'adj_open', u'CALM'), (u'adj_open', u'CALX'), (u'adj_open', u'CAMP'), (u'adj_open', u'CAR'), (u'adj_open', u'CARA'), (u'adj_open', u'CARB'), (u'adj_open', u'CASH'), (u'adj_open', u'CASS'), (u'adj_open', u'CASY'), (u'adj_open', u'CAT'), (u'adj_open', u'CATM'), (u'adj_open', u'CATO'), (u'adj_open', u'CATY'), (u'adj_open', u'CAVM'), (u'adj_open', u'CB'), (u'adj_open', u'CBB'), (u'adj_open', u'CBG'), (u'adj_open', u'CBI'), (u'adj_open', u'CBK'), (u'adj_open', u'CBL'), (u'adj_open', u'CBM'), (u'adj_open', u'CBOE'), (u'adj_open', u'CBPX'), (u'adj_open', u'CBRL'), (u'adj_open', u'CBS'), (u'adj_open', u'CBSH'), (u'adj_open', u'CBT'), (u'adj_open', u'CBU'), (u'adj_open', u'CBZ'), (u'adj_open', u'CCBG'), (u'adj_open', u'CCC'), (u'adj_open', u'CCE'), (u'adj_open', u'CCF'), (u'adj_open', u'CCI'), (u'adj_open', u'CCK'), (u'adj_open', u'CCL'), (u'adj_open', u'CCMP'), (u'adj_open', u'CCNE'), (u'adj_open', u'CCO'), (u'adj_open', u'CCOI'), (u'adj_open', u'CCRN'), (u'adj_open', u'CCXI'), (u'adj_open', u'CDE'), (u'adj_open', u'CDNS'), (u'adj_open', u'CDR'), (u'adj_open', u'CE'), (u'adj_open', u'CECE'), (u'adj_open', u'CECO'), (u'adj_open', u'CELG'), (u'adj_open', u'CENTA'), (u'adj_open', u'CENX'), (u'adj_open', u'CERN'), (u'adj_open', u'CERS'), (u'adj_open', u'CETV'), (u'adj_open', u'CEVA'), (u'adj_open', u'CF'), (u'adj_open', u'CFFI'), (u'adj_open', u'CFFN'), (u'adj_open', u'CFG'), (u'adj_open', u'CFR'), (u'adj_open', u'CFX'), (u'adj_open', u'CGI'), (u'adj_open', u'CGNX'), (u'adj_open', u'CHCO'), (u'adj_open', u'CHD'), (u'adj_open', u'CHDN'), (u'adj_open', u'CHE'), (u'adj_open', u'CHEF'), (u'adj_open', u'CHFC'), (u'adj_open', u'CHFN'), (u'adj_open', u'CHGG'), (u'adj_open', u'CHH'), (u'adj_open', u'CHK'), (u'adj_open', u'CHKP'), (u'adj_open', u'CHMG'), (u'adj_open', u'CHRW'), (u'adj_open', u'CHS'), (u'adj_open', u'CHSP'), (u'adj_open', u'CHTR'), (u'adj_open', u'CHUY'), (u'adj_open', u'CI'), (u'adj_open', u'CIA'), (u'adj_open', u'CIDM'), (u'adj_open', u'CIEN'), (u'adj_open', u'CIM'), (u'adj_open', u'CINF'), (u'adj_open', u'CIR'), (u'adj_open', u'CIT'), (u'adj_open', u'CIX'), (u'adj_open', u'CKH'), (u'adj_open', u'CL'), (u'adj_open', u'CLCT'), (u'adj_open', u'CLD'), (u'adj_open', u'CLDT'), (u'adj_open', u'CLDX'), (u'adj_open', u'CLF'), (u'adj_open', u'CLFD'), (u'adj_open', u'CLGX'), (u'adj_open', u'CLH'), (u'adj_open', u'CLI'), (u'adj_open', u'CLNE'), (u'adj_open', u'CLR'), (u'adj_open', u'CLUB'), (u'adj_open', u'CLVS'), (u'adj_open', u'CLW'), (u'adj_open', u'CLX'), (u'adj_open', u'CMA'), (u'adj_open', u'CMC'), (u'adj_open', u'CMCO'), (u'adj_open', u'CMCSA'), (u'adj_open', u'CME'), (u'adj_open', u'CMG'), (u'adj_open', u'CMI'), (u'close', u'A'), (u'close', u'AA'), (u'close', u'AAL'), (u'close', u'AAMC'), (u'close', u'AAN'), (u'close', u'AAOI'), (u'close', u'AAON'), (u'close', u'AAP'), (u'close', u'AAPL'), (u'close', u'AAT'), (u'close', u'AAWW'), (u'close', u'ABAX'), (u'close', u'ABBV'), (u'close', u'ABC'), (u'close', u'ABCB'), (u'close', u'ABG'), (u'close', u'ABM'), (u'close', u'ABMD'), (u'close', u'ABT'), (u'close', u'ACAD'), (u'close', u'ACC'), (u'close', u'ACCO'), (u'close', u'ACET'), (u'close', u'ACGL'), (u'close', u'ACHC'), (u'close', u'ACHN'), (u'close', u'ACIW'), (u'close', u'ACLS'), (u'close', u'ACM'), (u'close', u'ACN'), (u'close', u'ACOR'), (u'close', u'ACRE'), (u'close', u'ACRX'), (u'close', u'ACTG'), (u'close', u'ACXM'), (u'close', u'ADBE'), (u'close', u'ADC'), (u'close', u'ADES'), (u'close', u'ADI'), (u'close', u'ADM'), (u'close', u'ADMS'), (u'close', u'ADP'), (u'close', u'ADS'), (u'close', u'ADSK'), (u'close', u'ADTN'), (u'close', u'ADUS'), ...]
In [48]:
#Show summary statistics
print outg.describe()
open \
ticker A AA AAL AAMC AAN AAOI
count 21.000000 21.000000 21.000000 21.000000 21.00000 21.000000
mean 71.753810 54.271905 54.763810 166.567502 40.92619 35.652381
std 2.208258 1.319730 2.444354 233.518526 1.01858 1.897843
min 67.420000 52.340000 52.330000 66.950000 39.16000 32.740000
25% 70.680000 53.550000 52.790000 69.400000 39.98000 34.420000
50% 72.020000 53.960000 53.650000 75.950000 41.19000 34.980000
75% 73.770000 54.920000 57.740000 80.200000 41.80000 37.800000
max 74.480000 57.050000 58.790000 724.662246 42.50000 38.900000
... adj_close \
ticker AAON AAP AAPL AAT ... CLVS
count 21.000000 21.000000 21.000000 21.000000 ... 21.000000
mean 36.652381 114.896190 174.020952 36.495714 ... 61.407619
std 0.610425 5.812213 3.670836 0.890222 ... 3.657316
min 36.000000 100.900000 165.525000 34.900000 ... 54.930000
25% 36.200000 111.690000 172.530000 36.180000 ... 59.880000
50% 36.300000 115.670000 174.505000 36.340000 ... 61.400000
75% 36.950000 119.260000 177.250000 37.110000 ... 62.980000
max 37.950000 123.520000 179.370000 38.300000 ... 67.720000
\
ticker CLW CLX CMA CMC CMCO CMCSA
count 21.000000 21.000000 21.000000 21.000000 21.000000 21.000000
mean 48.400000 143.665714 92.572857 24.820952 42.260952 41.827143
std 1.343503 1.757668 3.212082 0.610393 1.144980 0.851758
min 46.150000 140.960000 86.540000 22.770000 40.280000 40.410000
25% 47.350000 142.250000 90.370000 24.590000 41.380000 41.070000
50% 48.400000 143.520000 93.810000 24.900000 42.470000 41.980000
75% 49.400000 144.990000 95.150000 25.160000 42.910000 42.500000
max 50.300000 146.440000 95.810000 25.820000 44.420000 42.990000
ticker CME CMG CMI
count 21.000000 21.000000 21.000000
mean 152.259524 324.301905 210.572488
std 2.699462 11.510498 117.866002
min 144.790000 292.950000 177.060000
25% 151.730000 319.370000 181.960000
50% 152.810000 327.340000 183.900000
75% 154.170000 330.230000 189.080000
max 155.450000 343.870000 724.662246
[8 rows x 1908 columns]
In [50]:
outg.plot(x='date', y='close', figsize=(12, 7), kind='bar')
Out[50]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fe5524eae50>
In [136]:
outg.set_index('date').query("Close Price").sort_values('Close Price', ascending=False).plot.bar(rot=0, width=1)
File "<unknown>", line 1 Close Price ^ SyntaxError: invalid syntax
In [ ]: